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class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.03616">arXiv:2502.03616</a> <span> [<a href="https://arxiv.org/pdf/2502.03616">pdf</a>, <a href="https://arxiv.org/format/2502.03616">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Science and Game Theory">cs.GT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> </div> </div> <p class="title is-5 mathjax"> Noncooperative Equilibrium Selection via a Trading-based Auction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Im%2C+J">Jaehan Im</a>, <a href="/search/cs?searchtype=author&query=Fotiadis%2C+F">Filippos Fotiadis</a>, <a href="/search/cs?searchtype=author&query=Delahaye%2C+D">Daniel Delahaye</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a>, <a href="/search/cs?searchtype=author&query=Fridovich-Keil%2C+D">David Fridovich-Keil</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.03616v1-abstract-short" style="display: inline;"> Noncooperative multi-agent systems often face coordination challenges due to conflicting preferences among agents. In particular, agents acting in their own self-interest can settle on different equilibria, leading to suboptimal outcomes or even safety concerns. We propose an algorithm named trading auction for consensus (TACo), a decentralized approach that enables noncooperative agents to reach… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.03616v1-abstract-full').style.display = 'inline'; document.getElementById('2502.03616v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.03616v1-abstract-full" style="display: none;"> Noncooperative multi-agent systems often face coordination challenges due to conflicting preferences among agents. In particular, agents acting in their own self-interest can settle on different equilibria, leading to suboptimal outcomes or even safety concerns. We propose an algorithm named trading auction for consensus (TACo), a decentralized approach that enables noncooperative agents to reach consensus without communicating directly or disclosing private valuations. TACo facilitates coordination through a structured trading-based auction, where agents iteratively select choices of interest and provably reach an agreement within an a priori bounded number of steps. A series of numerical experiments validate that the termination guarantees of TACo hold in practice, and show that TACo achieves a median performance that minimizes the total cost across all agents, while allocating resources significantly more fairly than baseline approaches. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.03616v1-abstract-full').style.display = 'none'; document.getElementById('2502.03616v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2502.01857">arXiv:2502.01857</a> <span> [<a href="https://arxiv.org/pdf/2502.01857">pdf</a>, <a href="https://arxiv.org/format/2502.01857">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Learning Human Perception Dynamics for Informative Robot Communication </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chen%2C+S">Shenghui Chen</a>, <a href="/search/cs?searchtype=author&query=Zhao%2C+R">Ruihan Zhao</a>, <a href="/search/cs?searchtype=author&query=Chinchali%2C+S">Sandeep Chinchali</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2502.01857v1-abstract-short" style="display: inline;"> Human-robot cooperative navigation is challenging in environments with incomplete information. We introduce CoNav-Maze, a simulated robotics environment where a robot navigates using local perception while a human operator provides guidance based on an inaccurate map. The robot can share its camera views to improve the operator's understanding of the environment. To enable efficient human-robot co… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.01857v1-abstract-full').style.display = 'inline'; document.getElementById('2502.01857v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2502.01857v1-abstract-full" style="display: none;"> Human-robot cooperative navigation is challenging in environments with incomplete information. We introduce CoNav-Maze, a simulated robotics environment where a robot navigates using local perception while a human operator provides guidance based on an inaccurate map. The robot can share its camera views to improve the operator's understanding of the environment. To enable efficient human-robot cooperation, we propose Information Gain Monte Carlo Tree Search (IG-MCTS), an online planning algorithm that balances autonomous movement and informative communication. Central to IG-MCTS is a neural human perception dynamics model that estimates how humans distill information from robot communications. We collect a dataset through a crowdsourced mapping task in CoNav-Maze and train this model using a fully convolutional architecture with data augmentation. User studies show that IG-MCTS outperforms teleoperation and instruction-following baselines, achieving comparable task performance with significantly less communication and lower human cognitive load, as evidenced by eye-tracking metrics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2502.01857v1-abstract-full').style.display = 'none'; document.getElementById('2502.01857v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.19398">arXiv:2501.19398</a> <span> [<a href="https://arxiv.org/pdf/2501.19398">pdf</a>, <a href="https://arxiv.org/format/2501.19398">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Science and Game Theory">cs.GT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Do LLMs Strategically Reveal, Conceal, and Infer Information? A Theoretical and Empirical Analysis in The Chameleon Game </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Karabag%2C+M+O">Mustafa O. Karabag</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.19398v1-abstract-short" style="display: inline;"> Large language model-based (LLM-based) agents have become common in settings that include non-cooperative parties. In such settings, agents' decision-making needs to conceal information from their adversaries, reveal information to their cooperators, and infer information to identify the other agents' characteristics. To investigate whether LLMs have these information control and decision-making c… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.19398v1-abstract-full').style.display = 'inline'; document.getElementById('2501.19398v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.19398v1-abstract-full" style="display: none;"> Large language model-based (LLM-based) agents have become common in settings that include non-cooperative parties. In such settings, agents' decision-making needs to conceal information from their adversaries, reveal information to their cooperators, and infer information to identify the other agents' characteristics. To investigate whether LLMs have these information control and decision-making capabilities, we make LLM agents play the language-based hidden-identity game, The Chameleon. In the game, a group of non-chameleon agents who do not know each other aim to identify the chameleon agent without revealing a secret. The game requires the aforementioned information control capabilities both as a chameleon and a non-chameleon. The empirical results show that while non-chameleon LLM agents identify the chameleon, they fail to conceal the secret from the chameleon, and their winning probability is far from the levels of even trivial strategies. To formally explain this behavior, we give a theoretical analysis for a spectrum of strategies, from concealing to revealing, and provide bounds on the non-chameleons' winning probability. Based on the empirical results and theoretical analysis of different strategies, we deduce that LLM-based non-chameleon agents reveal excessive information to agents of unknown identities. Our results point to a weakness of contemporary LLMs, including GPT-4, GPT-4o, Gemini 1.5, and Claude 3.5 Sonnet, in strategic interactions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.19398v1-abstract-full').style.display = 'none'; document.getElementById('2501.19398v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 31 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.18803">arXiv:2501.18803</a> <span> [<a href="https://arxiv.org/pdf/2501.18803">pdf</a>, <a href="https://arxiv.org/format/2501.18803">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> Deceptive Sequential Decision-Making via Regularized Policy Optimization </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kim%2C+Y">Yerin Kim</a>, <a href="/search/cs?searchtype=author&query=Benvenuti%2C+A">Alexander Benvenuti</a>, <a href="/search/cs?searchtype=author&query=Chen%2C+B">Bo Chen</a>, <a href="/search/cs?searchtype=author&query=Karabag%2C+M">Mustafa Karabag</a>, <a href="/search/cs?searchtype=author&query=Kulkarni%2C+A">Abhishek Kulkarni</a>, <a href="/search/cs?searchtype=author&query=Bastian%2C+N+D">Nathaniel D. Bastian</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a>, <a href="/search/cs?searchtype=author&query=Hale%2C+M">Matthew Hale</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.18803v1-abstract-short" style="display: inline;"> Autonomous systems are increasingly expected to operate in the presence of adversaries, though an adversary may infer sensitive information simply by observing a system, without even needing to interact with it. Therefore, in this work we present a deceptive decision-making framework that not only conceals sensitive information, but in fact actively misleads adversaries about it. We model autonomo… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.18803v1-abstract-full').style.display = 'inline'; document.getElementById('2501.18803v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.18803v1-abstract-full" style="display: none;"> Autonomous systems are increasingly expected to operate in the presence of adversaries, though an adversary may infer sensitive information simply by observing a system, without even needing to interact with it. Therefore, in this work we present a deceptive decision-making framework that not only conceals sensitive information, but in fact actively misleads adversaries about it. We model autonomous systems as Markov decision processes, and we consider adversaries that attempt to infer their reward functions using inverse reinforcement learning. To counter such efforts, we present two regularization strategies for policy synthesis problems that actively deceive an adversary about a system's underlying rewards. The first form of deception is ``diversionary'', and it leads an adversary to draw any false conclusion about what the system's reward function is. The second form of deception is ``targeted'', and it leads an adversary to draw a specific false conclusion about what the system's reward function is. We then show how each form of deception can be implemented in policy optimization problems, and we analytically bound the loss in total accumulated reward that is induced by deception. Next, we evaluate these developments in a multi-agent sequential decision-making problem with one real agent and multiple decoys. We show that diversionary deception can cause the adversary to believe that the most important agent is the least important, while attaining a total accumulated reward that is $98.83\%$ of its optimal, non-deceptive value. Similarly, we show that targeted deception can make any decoy appear to be the most important agent, while still attaining a total accumulated reward that is $99.25\%$ of its optimal, non-deceptive value. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.18803v1-abstract-full').style.display = 'none'; document.getElementById('2501.18803v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">21 pages, 5 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.18373">arXiv:2501.18373</a> <span> [<a href="https://arxiv.org/pdf/2501.18373">pdf</a>, <a href="https://arxiv.org/format/2501.18373">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Function Encoders: A Principled Approach to Transfer Learning in Hilbert Spaces </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ingebrand%2C+T">Tyler Ingebrand</a>, <a href="/search/cs?searchtype=author&query=Thorpe%2C+A+J">Adam J. Thorpe</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.18373v1-abstract-short" style="display: inline;"> A central challenge in transfer learning is designing algorithms that can quickly adapt and generalize to new tasks without retraining. Yet, the conditions of when and how algorithms can effectively transfer to new tasks is poorly characterized. We introduce a geometric characterization of transfer in Hilbert spaces and define three types of inductive transfer: interpolation within the convex hull… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.18373v1-abstract-full').style.display = 'inline'; document.getElementById('2501.18373v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.18373v1-abstract-full" style="display: none;"> A central challenge in transfer learning is designing algorithms that can quickly adapt and generalize to new tasks without retraining. Yet, the conditions of when and how algorithms can effectively transfer to new tasks is poorly characterized. We introduce a geometric characterization of transfer in Hilbert spaces and define three types of inductive transfer: interpolation within the convex hull, extrapolation to the linear span, and extrapolation outside the span. We propose a method grounded in the theory of function encoders to achieve all three types of transfer. Specifically, we introduce a novel training scheme for function encoders using least-squares optimization, prove a universal approximation theorem for function encoders, and provide a comprehensive comparison with existing approaches such as transformers and meta-learning on four diverse benchmarks. Our experiments demonstrate that the function encoder outperforms state-of-the-art methods on four benchmark tasks and on all three types of transfer. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.18373v1-abstract-full').style.display = 'none'; document.getElementById('2501.18373v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 30 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.18022">arXiv:2501.18022</a> <span> [<a href="https://arxiv.org/pdf/2501.18022">pdf</a>, <a href="https://arxiv.org/ps/2501.18022">ps</a>, <a href="https://arxiv.org/format/2501.18022">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Science and Game Theory">cs.GT</span> </div> </div> <p class="title is-5 mathjax"> Dynamic Coalitions in Games on Graphs with Preferences over Temporal Goals </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yilmaz%2C+A+K+A">A. Kaan Ata Yilmaz</a>, <a href="/search/cs?searchtype=author&query=Kulkarni%2C+A">Abhishek Kulkarni</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.18022v1-abstract-short" style="display: inline;"> In multiplayer games with sequential decision-making, self-interested players form dynamic coalitions to achieve most-preferred temporal goals beyond their individual capabilities. We introduce a novel procedure to synthesize strategies that jointly determine which coalitions should form and the actions coalition members should choose to satisfy their preferences in a subclass of deterministic mul… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.18022v1-abstract-full').style.display = 'inline'; document.getElementById('2501.18022v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.18022v1-abstract-full" style="display: none;"> In multiplayer games with sequential decision-making, self-interested players form dynamic coalitions to achieve most-preferred temporal goals beyond their individual capabilities. We introduce a novel procedure to synthesize strategies that jointly determine which coalitions should form and the actions coalition members should choose to satisfy their preferences in a subclass of deterministic multiplayer games on graphs. In these games, a leader decides the coalition during each round and the players not in the coalition follow their admissible strategies. Our contributions are threefold. First, we extend the concept of admissibility to games on graphs with preferences and characterize it using maximal sure winning, a concept originally defined for adversarial two-player games with preferences. Second, we define a value function that assigns a vector to each state, identifying which player has a maximal sure winning strategy for certain subset of objectives. Finally, we present a polynomial-time algorithm to synthesize admissible strategies for all players based on this value function and prove their existence in all games within the chosen subclass. We illustrate the benefits of dynamic coalitions over fixed ones in a blocks-world domain. Interestingly, our experiment reveals that aligned preferences do not always encourage cooperation, while conflicting preferences do not always lead to adversarial behavior. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.18022v1-abstract-full').style.display = 'none'; document.getElementById('2501.18022v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 29 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages, 3 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.16307">arXiv:2501.16307</a> <span> [<a href="https://arxiv.org/pdf/2501.16307">pdf</a>, <a href="https://arxiv.org/format/2501.16307">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Science and Game Theory">cs.GT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Logic in Computer Science">cs.LO</span> </div> </div> <p class="title is-5 mathjax"> Privacy-aware Nash Equilibrium Synthesis with Partially Ordered LTL$_f$ Objectives </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Probine%2C+C">Caleb Probine</a>, <a href="/search/cs?searchtype=author&query=Kulkarni%2C+A">Abhishek Kulkarni</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.16307v1-abstract-short" style="display: inline;"> Nash equilibrium is a fundamental solution concept for modeling the behavior of self-interested agents. We develop an algorithm to synthesize pure Nash equilibria in two-player deterministic games on graphs where players have partial preferences over objectives expressed with linear temporal logic over finite traces. Previous approaches for Nash equilibrium synthesis assume that players' preferenc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.16307v1-abstract-full').style.display = 'inline'; document.getElementById('2501.16307v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.16307v1-abstract-full" style="display: none;"> Nash equilibrium is a fundamental solution concept for modeling the behavior of self-interested agents. We develop an algorithm to synthesize pure Nash equilibria in two-player deterministic games on graphs where players have partial preferences over objectives expressed with linear temporal logic over finite traces. Previous approaches for Nash equilibrium synthesis assume that players' preferences are common knowledge. Instead, we allow players' preferences to remain private but enable communication between players. The algorithm we design synthesizes Nash equilibria for a complete-information game, but synthesizes these equilibria in an incomplete-information setting where players' preferences are private. The algorithm is privacy-aware, as instead of requiring that players share private preferences, the algorithm reduces the information sharing to a query interface. Through this interface, players exchange information about states in the game from which they can enforce a more desirable outcome. We prove the algorithm's completeness by showing that it either returns an equilibrium or certifies that one does not exist. We then demonstrate, via numerical examples, the existence of games where the queries the players exchange are insufficient to reconstruct players' preferences, highlighting the privacy-aware nature of the algorithm we propose. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.16307v1-abstract-full').style.display = 'none'; document.getElementById('2501.16307v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">13 pages, 6 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.16291">arXiv:2501.16291</a> <span> [<a href="https://arxiv.org/pdf/2501.16291">pdf</a>, <a href="https://arxiv.org/format/2501.16291">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Science and Game Theory">cs.GT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Formal Languages and Automata Theory">cs.FL</span> </div> </div> <p class="title is-5 mathjax"> Sequential Decision Making in Stochastic Games with Incomplete Preferences over Temporal Objectives </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kulkarni%2C+A+N">Abhishek Ninad Kulkarni</a>, <a href="/search/cs?searchtype=author&query=Fu%2C+J">Jie Fu</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.16291v1-abstract-short" style="display: inline;"> Ensuring that AI systems make strategic decisions aligned with the specified preferences in adversarial sequential interactions is a critical challenge for developing trustworthy AI systems, especially when the environment is stochastic and players' incomplete preferences leave some outcomes unranked. We study the problem of synthesizing preference-satisfying strategies in two-player stochastic ga… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.16291v1-abstract-full').style.display = 'inline'; document.getElementById('2501.16291v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.16291v1-abstract-full" style="display: none;"> Ensuring that AI systems make strategic decisions aligned with the specified preferences in adversarial sequential interactions is a critical challenge for developing trustworthy AI systems, especially when the environment is stochastic and players' incomplete preferences leave some outcomes unranked. We study the problem of synthesizing preference-satisfying strategies in two-player stochastic games on graphs where players have opposite (possibly incomplete) preferences over a set of temporal goals. We represent these goals using linear temporal logic over finite traces (LTLf), which enables modeling the nuances of human preferences where temporal goals need not be mutually exclusive and comparison between some goals may be unspecified. We introduce a solution concept of non-dominated almost-sure winning, which guarantees to achieve a most preferred outcome aligned with specified preferences while maintaining robustness against the adversarial behaviors of the opponent. Our results show that strategy profiles based on this concept are Nash equilibria in the game where players are risk-averse, thus providing a practical framework for evaluating and ensuring stable, preference-aligned outcomes in the game. Using a drone delivery example, we demonstrate that our contributions offer valuable insights not only for synthesizing rational behavior under incomplete preferences but also for designing games that motivate the desired behavior from the players in adversarial conditions. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.16291v1-abstract-full').style.display = 'none'; document.getElementById('2501.16291v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 27 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">9 pages, 3 figures, accepted at AAAI 2025 (AI alignment track)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.08941">arXiv:2501.08941</a> <span> [<a href="https://arxiv.org/pdf/2501.08941">pdf</a>, <a href="https://arxiv.org/format/2501.08941">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.2514/6.2025-2118">10.2514/6.2025-2118 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> A Reinforcement Learning Approach to Quiet and Safe UAM Traffic Management </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Murthy%2C+S">Surya Murthy</a>, <a href="/search/cs?searchtype=author&query=Clarke%2C+J">John-Paul Clarke</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a>, <a href="/search/cs?searchtype=author&query=Gao%2C+Z">Zhenyu Gao</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.08941v1-abstract-short" style="display: inline;"> Urban air mobility (UAM) is a transformative system that operates various small aerial vehicles in urban environments to reshape urban transportation. However, integrating UAM into existing urban environments presents a variety of complex challenges. Recent analyses of UAM's operational constraints highlight aircraft noise and system safety as key hurdles to UAM system implementation. Future UAM a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.08941v1-abstract-full').style.display = 'inline'; document.getElementById('2501.08941v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.08941v1-abstract-full" style="display: none;"> Urban air mobility (UAM) is a transformative system that operates various small aerial vehicles in urban environments to reshape urban transportation. However, integrating UAM into existing urban environments presents a variety of complex challenges. Recent analyses of UAM's operational constraints highlight aircraft noise and system safety as key hurdles to UAM system implementation. Future UAM air traffic management schemes must ensure that the system is both quiet and safe. We propose a multi-agent reinforcement learning approach to manage UAM traffic, aiming at both vertical separation assurance and noise mitigation. Through extensive training, the reinforcement learning agent learns to balance the two primary objectives by employing altitude adjustments in a multi-layer UAM network. The results reveal the tradeoffs among noise impact, traffic congestion, and separation. Overall, our findings demonstrate the potential of reinforcement learning in mitigating UAM's noise impact while maintaining safe separation using altitude adjustments <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.08941v1-abstract-full').style.display = 'none'; document.getElementById('2501.08941v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Paper presented at SciTech 2025</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> AIAA SciTech 2025 Forum </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2501.08933">arXiv:2501.08933</a> <span> [<a href="https://arxiv.org/pdf/2501.08933">pdf</a>, <a href="https://arxiv.org/format/2501.08933">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> </div> <div class="is-inline-block" style="margin-left: 0.5rem"> <div class="tags has-addons"> <span class="tag is-dark is-size-7">doi</span> <span class="tag is-light is-size-7"><a class="" href="https://doi.org/10.2514/6.2025-2116">10.2514/6.2025-2116 <i class="fa fa-external-link" aria-hidden="true"></i></a></span> </div> </div> </div> <p class="title is-5 mathjax"> Separation Assurance in Urban Air Mobility Systems using Shared Scheduling Protocols </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Murthy%2C+S">Surya Murthy</a>, <a href="/search/cs?searchtype=author&query=Ingebrand%2C+T">Tyler Ingebrand</a>, <a href="/search/cs?searchtype=author&query=Smith%2C+S">Sophia Smith</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a>, <a href="/search/cs?searchtype=author&query=Wei%2C+P">Peng Wei</a>, <a href="/search/cs?searchtype=author&query=Neogi%2C+N">Natasha Neogi</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2501.08933v1-abstract-short" style="display: inline;"> Ensuring safe separation between aircraft is a critical challenge in air traffic management, particularly in urban air mobility (UAM) environments where high traffic density and low altitudes require precise control. In these environments, conflicts often arise at the intersections of flight corridors, posing significant risks. We propose a tactical separation approach leveraging shared scheduling… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.08933v1-abstract-full').style.display = 'inline'; document.getElementById('2501.08933v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2501.08933v1-abstract-full" style="display: none;"> Ensuring safe separation between aircraft is a critical challenge in air traffic management, particularly in urban air mobility (UAM) environments where high traffic density and low altitudes require precise control. In these environments, conflicts often arise at the intersections of flight corridors, posing significant risks. We propose a tactical separation approach leveraging shared scheduling protocols, originally designed for Ethernet networks and operating systems, to coordinate access to these intersections. Using a decentralized Markov decision process framework, the proposed approach enables aircraft to autonomously adjust their speed and timing as they navigate these critical areas, maintaining safe separation without a central controller. We evaluate the effectiveness of this approach in simulated UAM scenarios, demonstrating its ability to reduce separation violations to zero while acknowledging trade-offs in flight times as traffic density increases. Additionally, we explore the impact of non-compliant aircraft, showing that while shared scheduling protocols can no longer guarantee safe separation, they still provide significant improvements over systems without scheduling protocols. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2501.08933v1-abstract-full').style.display = 'none'; document.getElementById('2501.08933v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 January, 2025; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2025. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Paper presented in 2025 AIAA SciTech</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> AIAA SciTech 2025 AIAA SciTech 2025 AIAA SciTech 2025 Forum </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.11215">arXiv:2412.11215</a> <span> [<a href="https://arxiv.org/pdf/2412.11215">pdf</a>, <a href="https://arxiv.org/format/2412.11215">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Neural Port-Hamiltonian Differential Algebraic Equations for Compositional Learning of Electrical Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Neary%2C+C">Cyrus Neary</a>, <a href="/search/cs?searchtype=author&query=Tsao%2C+N">Nathan Tsao</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.11215v1-abstract-short" style="display: inline;"> We develop compositional learning algorithms for coupled dynamical systems. While deep learning has proven effective at modeling complex relationships from data, compositional couplings between system components typically introduce algebraic constraints on state variables, posing challenges to many existing data-driven approaches to modeling dynamical systems. Towards developing deep learning mode… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.11215v1-abstract-full').style.display = 'inline'; document.getElementById('2412.11215v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.11215v1-abstract-full" style="display: none;"> We develop compositional learning algorithms for coupled dynamical systems. While deep learning has proven effective at modeling complex relationships from data, compositional couplings between system components typically introduce algebraic constraints on state variables, posing challenges to many existing data-driven approaches to modeling dynamical systems. Towards developing deep learning models for constrained dynamical systems, we introduce neural port-Hamiltonian differential algebraic equations (N-PHDAEs), which use neural networks to parametrize unknown terms in both the differential and algebraic components of a port-Hamiltonian DAE. To train these models, we propose an algorithm that uses automatic differentiation to perform index reduction, automatically transforming the neural DAE into an equivalent system of neural ordinary differential equations (N-ODEs), for which established model inference and backpropagation methods exist. The proposed compositional modeling framework and learning algorithms may be applied broadly to learn control-oriented models of dynamical systems in a variety of application areas, however, in this work, we focus on their application to the modeling of electrical networks. Experiments simulating the dynamics of nonlinear circuits exemplify the benefits of our approach: the proposed N-PHDAE model achieves an order of magnitude improvement in prediction accuracy and constraint satisfaction when compared to a baseline N-ODE over long prediction time horizons. We also validate the compositional capabilities of our approach through experiments on a simulated D.C. microgrid: we train individual N-PHDAE models for separate grid components, before coupling them to accurately predict the behavior of larger-scale networks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.11215v1-abstract-full').style.display = 'none'; document.getElementById('2412.11215v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2412.01114">arXiv:2412.01114</a> <span> [<a href="https://arxiv.org/pdf/2412.01114">pdf</a>, <a href="https://arxiv.org/format/2412.01114">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Dense Dynamics-Aware Reward Synthesis: Integrating Prior Experience with Demonstrations </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Koprulu%2C+C">Cevahir Koprulu</a>, <a href="/search/cs?searchtype=author&query=Li%2C+P">Po-han Li</a>, <a href="/search/cs?searchtype=author&query=Qiu%2C+T">Tianyu Qiu</a>, <a href="/search/cs?searchtype=author&query=Zhao%2C+R">Ruihan Zhao</a>, <a href="/search/cs?searchtype=author&query=Westenbroek%2C+T">Tyler Westenbroek</a>, <a href="/search/cs?searchtype=author&query=Fridovich-Keil%2C+D">David Fridovich-Keil</a>, <a href="/search/cs?searchtype=author&query=Chinchali%2C+S">Sandeep Chinchali</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2412.01114v1-abstract-short" style="display: inline;"> Many continuous control problems can be formulated as sparse-reward reinforcement learning (RL) tasks. In principle, online RL methods can automatically explore the state space to solve each new task. However, discovering sequences of actions that lead to a non-zero reward becomes exponentially more difficult as the task horizon increases. Manually shaping rewards can accelerate learning for a fix… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.01114v1-abstract-full').style.display = 'inline'; document.getElementById('2412.01114v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2412.01114v1-abstract-full" style="display: none;"> Many continuous control problems can be formulated as sparse-reward reinforcement learning (RL) tasks. In principle, online RL methods can automatically explore the state space to solve each new task. However, discovering sequences of actions that lead to a non-zero reward becomes exponentially more difficult as the task horizon increases. Manually shaping rewards can accelerate learning for a fixed task, but it is an arduous process that must be repeated for each new environment. We introduce a systematic reward-shaping framework that distills the information contained in 1) a task-agnostic prior data set and 2) a small number of task-specific expert demonstrations, and then uses these priors to synthesize dense dynamics-aware rewards for the given task. This supervision substantially accelerates learning in our experiments, and we provide analysis demonstrating how the approach can effectively guide online learning agents to faraway goals. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2412.01114v1-abstract-full').style.display = 'none'; document.getElementById('2412.01114v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 December, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.15677">arXiv:2411.15677</a> <span> [<a href="https://arxiv.org/pdf/2411.15677">pdf</a>, <a href="https://arxiv.org/format/2411.15677">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> How Media Competition Fuels the Spread of Misinformation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Amini%2C+A">Arash Amini</a>, <a href="/search/cs?searchtype=author&query=Bayiz%2C+Y+E">Yigit Ege Bayiz</a>, <a href="/search/cs?searchtype=author&query=Lee%2C+E">Eun-Ju Lee</a>, <a href="/search/cs?searchtype=author&query=Somer-Topcu%2C+Z">Zeynep Somer-Topcu</a>, <a href="/search/cs?searchtype=author&query=Marculescu%2C+R">Radu Marculescu</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.15677v1-abstract-short" style="display: inline;"> Competition among news sources may encourage some sources to share fake news and misinformation to influence the public. While sharing misinformation may lead to a short-term gain in audience engagement, it may damage the reputation of these sources, resulting in a loss of audience. To understand the rationale behind sharing misinformation, we model the competition as a zero-sum sequential game, w… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.15677v1-abstract-full').style.display = 'inline'; document.getElementById('2411.15677v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.15677v1-abstract-full" style="display: none;"> Competition among news sources may encourage some sources to share fake news and misinformation to influence the public. While sharing misinformation may lead to a short-term gain in audience engagement, it may damage the reputation of these sources, resulting in a loss of audience. To understand the rationale behind sharing misinformation, we model the competition as a zero-sum sequential game, where each news source influences individuals based on its credibility-how trustworthy the public perceives it-and the individual's opinion and susceptibility. In this game, news sources can decide whether to share factual information to enhance their credibility or disseminate misinformation for greater immediate attention at the cost of losing credibility. We employ the quantal response equilibrium concept, which accounts for the bounded rationality of human decision-making, allowing for imperfect or probabilistic choices. Our analysis shows that the resulting equilibria for this game reproduce the credibility-bias distribution observed in real-world news sources, with hyper-partisan sources more likely to spread misinformation than centrist ones. It further illustrates that disseminating misinformation can polarize the public. Notably, our model reveals that when one player increases misinformation dissemination, the other player is likely to follow, exacerbating the spread of misinformation. We conclude by discussing potential strategies to mitigate the spread of fake news and promote a more factual and reliable information landscape. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.15677v1-abstract-full').style.display = 'none'; document.getElementById('2411.15677v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">18 pages, 8 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.10513">arXiv:2411.10513</a> <span> [<a href="https://arxiv.org/pdf/2411.10513">pdf</a>, <a href="https://arxiv.org/format/2411.10513">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multimedia">cs.MM</span> </div> </div> <p class="title is-5 mathjax"> Any2Any: Incomplete Multimodal Retrieval with Conformal Prediction </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Li%2C+P">Po-han Li</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+Y">Yunhao Yang</a>, <a href="/search/cs?searchtype=author&query=Omama%2C+M">Mohammad Omama</a>, <a href="/search/cs?searchtype=author&query=Chinchali%2C+S">Sandeep Chinchali</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.10513v2-abstract-short" style="display: inline;"> Autonomous agents perceive and interpret their surroundings by integrating multimodal inputs, such as vision, audio, and LiDAR. These perceptual modalities support retrieval tasks, such as place recognition in robotics. However, current multimodal retrieval systems encounter difficulties when parts of the data are missing due to sensor failures or inaccessibility, such as silent videos or LiDAR sc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10513v2-abstract-full').style.display = 'inline'; document.getElementById('2411.10513v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.10513v2-abstract-full" style="display: none;"> Autonomous agents perceive and interpret their surroundings by integrating multimodal inputs, such as vision, audio, and LiDAR. These perceptual modalities support retrieval tasks, such as place recognition in robotics. However, current multimodal retrieval systems encounter difficulties when parts of the data are missing due to sensor failures or inaccessibility, such as silent videos or LiDAR scans lacking RGB information. We propose Any2Any-a novel retrieval framework that addresses scenarios where both query and reference instances have incomplete modalities. Unlike previous methods limited to the imputation of two modalities, Any2Any handles any number of modalities without training generative models. It calculates pairwise similarities with cross-modal encoders and employs a two-stage calibration process with conformal prediction to align the similarities. Any2Any enables effective retrieval across multimodal datasets, e.g., text-LiDAR and text-time series. It achieves a Recall@5 of 35% on the KITTI dataset, which is on par with baseline models with complete modalities. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.10513v2-abstract-full').style.display = 'none'; document.getElementById('2411.10513v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2411.01639">arXiv:2411.01639</a> <span> [<a href="https://arxiv.org/pdf/2411.01639">pdf</a>, <a href="https://arxiv.org/format/2411.01639">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Know Where You're Uncertain When Planning with Multimodal Foundation Models: A Formal Framework </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bhatt%2C+N+P">Neel P. Bhatt</a>, <a href="/search/cs?searchtype=author&query=Yang%2C+Y">Yunhao Yang</a>, <a href="/search/cs?searchtype=author&query=Siva%2C+R">Rohan Siva</a>, <a href="/search/cs?searchtype=author&query=Milan%2C+D">Daniel Milan</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+Z">Zhangyang Wang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2411.01639v1-abstract-short" style="display: inline;"> Multimodal foundation models offer a promising framework for robotic perception and planning by processing sensory inputs to generate actionable plans. However, addressing uncertainty in both perception (sensory interpretation) and decision-making (plan generation) remains a critical challenge for ensuring task reliability. We present a comprehensive framework to disentangle, quantify, and mitigat… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.01639v1-abstract-full').style.display = 'inline'; document.getElementById('2411.01639v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2411.01639v1-abstract-full" style="display: none;"> Multimodal foundation models offer a promising framework for robotic perception and planning by processing sensory inputs to generate actionable plans. However, addressing uncertainty in both perception (sensory interpretation) and decision-making (plan generation) remains a critical challenge for ensuring task reliability. We present a comprehensive framework to disentangle, quantify, and mitigate these two forms of uncertainty. We first introduce a framework for uncertainty disentanglement, isolating perception uncertainty arising from limitations in visual understanding and decision uncertainty relating to the robustness of generated plans. To quantify each type of uncertainty, we propose methods tailored to the unique properties of perception and decision-making: we use conformal prediction to calibrate perception uncertainty and introduce Formal-Methods-Driven Prediction (FMDP) to quantify decision uncertainty, leveraging formal verification techniques for theoretical guarantees. Building on this quantification, we implement two targeted intervention mechanisms: an active sensing process that dynamically re-observes high-uncertainty scenes to enhance visual input quality and an automated refinement procedure that fine-tunes the model on high-certainty data, improving its capability to meet task specifications. Empirical validation in real-world and simulated robotic tasks demonstrates that our uncertainty disentanglement framework reduces variability by up to 40% and enhances task success rates by 5% compared to baselines. These improvements are attributed to the combined effect of both interventions and highlight the importance of uncertainty disentanglement which facilitates targeted interventions that enhance the robustness and reliability of autonomous systems. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2411.01639v1-abstract-full').style.display = 'none'; document.getElementById('2411.01639v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Fine-tuned models, code, and datasets are available at https://tinyurl.com/uncertainty-disentanglement</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.18242">arXiv:2410.18242</a> <span> [<a href="https://arxiv.org/pdf/2410.18242">pdf</a>, <a href="https://arxiv.org/format/2410.18242">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> </div> </div> <p class="title is-5 mathjax"> Human-Agent Coordination in Games under Incomplete Information via Multi-Step Intent </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chen%2C+S">Shenghui Chen</a>, <a href="/search/cs?searchtype=author&query=Zhao%2C+R">Ruihan Zhao</a>, <a href="/search/cs?searchtype=author&query=Chinchali%2C+S">Sandeep Chinchali</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.18242v2-abstract-short" style="display: inline;"> Strategic coordination between autonomous agents and human partners under incomplete information can be modeled as turn-based cooperative games. We extend a turn-based game under incomplete information, the shared-control game, to allow players to take multiple actions per turn rather than a single action. The extension enables the use of multi-step intent, which we hypothesize will improve perfor… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.18242v2-abstract-full').style.display = 'inline'; document.getElementById('2410.18242v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.18242v2-abstract-full" style="display: none;"> Strategic coordination between autonomous agents and human partners under incomplete information can be modeled as turn-based cooperative games. We extend a turn-based game under incomplete information, the shared-control game, to allow players to take multiple actions per turn rather than a single action. The extension enables the use of multi-step intent, which we hypothesize will improve performance in long-horizon tasks. To synthesize cooperative policies for the agent in this extended game, we propose an approach featuring a memory module for a running probabilistic belief of the environment dynamics and an online planning algorithm called IntentMCTS. This algorithm strategically selects the next action by leveraging any communicated multi-step intent via reward augmentation while considering the current belief. Agent-to-agent simulations in the Gnomes at Night testbed demonstrate that IntentMCTS requires fewer steps and control switches than baseline methods. A human-agent user study corroborates these findings, showing an 18.52% higher success rate compared to the heuristic baseline and a 5.56% improvement over the single-step prior work. Participants also report lower cognitive load, frustration, and higher satisfaction with the IntentMCTS agent partner. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.18242v2-abstract-full').style.display = 'none'; document.getElementById('2410.18242v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 23 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.16441">arXiv:2410.16441</a> <span> [<a href="https://arxiv.org/pdf/2410.16441">pdf</a>, <a href="https://arxiv.org/format/2410.16441">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Science and Game Theory">cs.GT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Policies with Sparse Inter-Agent Dependencies in Dynamic Games: A Dynamic Programming Approach </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Liu%2C+X">Xinjie Liu</a>, <a href="/search/cs?searchtype=author&query=Li%2C+J">Jingqi Li</a>, <a href="/search/cs?searchtype=author&query=Fotiadis%2C+F">Filippos Fotiadis</a>, <a href="/search/cs?searchtype=author&query=Karabag%2C+M+O">Mustafa O. Karabag</a>, <a href="/search/cs?searchtype=author&query=Milzman%2C+J">Jesse Milzman</a>, <a href="/search/cs?searchtype=author&query=Fridovich-Keil%2C+D">David Fridovich-Keil</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.16441v1-abstract-short" style="display: inline;"> Common feedback strategies in multi-agent dynamic games require all players' state information to compute control strategies. However, in real-world scenarios, sensing and communication limitations between agents make full state feedback expensive or impractical, and such strategies can become fragile when state information from other agents is inaccurate. To this end, we propose a regularized dyn… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16441v1-abstract-full').style.display = 'inline'; document.getElementById('2410.16441v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.16441v1-abstract-full" style="display: none;"> Common feedback strategies in multi-agent dynamic games require all players' state information to compute control strategies. However, in real-world scenarios, sensing and communication limitations between agents make full state feedback expensive or impractical, and such strategies can become fragile when state information from other agents is inaccurate. To this end, we propose a regularized dynamic programming approach for finding sparse feedback policies that selectively depend on the states of a subset of agents in dynamic games. The proposed approach solves convex adaptive group Lasso problems to compute sparse policies approximating Nash equilibrium solutions. We prove the regularized solutions' asymptotic convergence to a neighborhood of Nash equilibrium policies in linear-quadratic (LQ) games. We extend the proposed approach to general non-LQ games via an iterative algorithm. Empirical results in multi-robot interaction scenarios show that the proposed approach effectively computes feedback policies with varying sparsity levels. When agents have noisy observations of other agents' states, simulation results indicate that the proposed regularized policies consistently achieve lower costs than standard Nash equilibrium policies by up to 77% for all interacting agents whose costs are coupled with other agents' states. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.16441v1-abstract-full').style.display = 'none'; document.getElementById('2410.16441v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.14890">arXiv:2410.14890</a> <span> [<a href="https://arxiv.org/pdf/2410.14890">pdf</a>, <a href="https://arxiv.org/format/2410.14890">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Reasoning, Memorization, and Fine-Tuning Language Models for Non-Cooperative Games </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yang%2C+Y">Yunhao Yang</a>, <a href="/search/cs?searchtype=author&query=Berthellemy%2C+L">Leonard Berthellemy</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.14890v1-abstract-short" style="display: inline;"> We develop a method that integrates the tree of thoughts and multi-agent framework to enhance the capability of pre-trained language models in solving complex, unfamiliar games. The method decomposes game-solving into four incremental tasks -- game summarization, area selection, action extraction, and action validation -- each assigned to a specific language-model agent. By constructing a tree of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.14890v1-abstract-full').style.display = 'inline'; document.getElementById('2410.14890v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.14890v1-abstract-full" style="display: none;"> We develop a method that integrates the tree of thoughts and multi-agent framework to enhance the capability of pre-trained language models in solving complex, unfamiliar games. The method decomposes game-solving into four incremental tasks -- game summarization, area selection, action extraction, and action validation -- each assigned to a specific language-model agent. By constructing a tree of thoughts, the method simulates reasoning paths and allows agents to collaboratively distill game representations and tactics, mitigating the limitations of language models in reasoning and long-term memorization. Additionally, an automated fine-tuning process further optimizes the agents' performance by ranking query-response pairs based on game outcomes, e.g., winning or losing. We apply the method to a non-cooperative game and demonstrate a 65 percent winning rate against benchmark algorithms, with an additional 10 percent improvement after fine-tuning. In contrast to existing deep learning algorithms for game solving that require millions of training samples, the proposed method consumes approximately 1000 training samples, highlighting its efficiency and scalability. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.14890v1-abstract-full').style.display = 'none'; document.getElementById('2410.14890v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.14865">arXiv:2410.14865</a> <span> [<a href="https://arxiv.org/pdf/2410.14865">pdf</a>, <a href="https://arxiv.org/format/2410.14865">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Formal Languages and Automata Theory">cs.FL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Joint Verification and Refinement of Language Models for Safety-Constrained Planning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yang%2C+Y">Yunhao Yang</a>, <a href="/search/cs?searchtype=author&query=Ward%2C+W">William Ward</a>, <a href="/search/cs?searchtype=author&query=Hu%2C+Z">Zichao Hu</a>, <a href="/search/cs?searchtype=author&query=Biswas%2C+J">Joydeep Biswas</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.14865v1-abstract-short" style="display: inline;"> Although pre-trained language models can generate executable plans (e.g., programmatic policies) for solving robot tasks, the generated plans may violate task-relevant logical specifications due to the models' black-box nature. A significant gap remains between the language models' outputs and verifiable executions of plans. We develop a method to generate executable plans and formally verify them… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.14865v1-abstract-full').style.display = 'inline'; document.getElementById('2410.14865v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.14865v1-abstract-full" style="display: none;"> Although pre-trained language models can generate executable plans (e.g., programmatic policies) for solving robot tasks, the generated plans may violate task-relevant logical specifications due to the models' black-box nature. A significant gap remains between the language models' outputs and verifiable executions of plans. We develop a method to generate executable plans and formally verify them against task-relevant safety specifications. Given a high-level task description in natural language, the proposed method queries a language model to generate plans in the form of executable robot programs. It then converts the generated plan into an automaton-based representation, allowing formal verification of the automaton against the specifications. We prove that given a set of verified plans, the composition of these plans also satisfies the safety specifications. This proof ensures the safety of complex, multi-component plans, obviating the computation complexity of verifying the composed plan. We then propose an automated fine-tuning process that refines the language model to generate specification-compliant plans without the need for human labeling. The empirical results show a 30 percent improvement in the probability of generating plans that meet task specifications after fine-tuning. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.14865v1-abstract-full').style.display = 'none'; document.getElementById('2410.14865v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.07610">arXiv:2410.07610</a> <span> [<a href="https://arxiv.org/pdf/2410.07610">pdf</a>, <a href="https://arxiv.org/format/2410.07610">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Information Retrieval">cs.IR</span> </div> </div> <p class="title is-5 mathjax"> CSA: Data-efficient Mapping of Unimodal Features to Multimodal Features </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Li%2C+P">Po-han Li</a>, <a href="/search/cs?searchtype=author&query=Chinchali%2C+S+P">Sandeep P. Chinchali</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.07610v3-abstract-short" style="display: inline;"> Multimodal encoders like CLIP excel in tasks such as zero-shot image classification and cross-modal retrieval. However, they require excessive training data. We propose canonical similarity analysis (CSA), which uses two unimodal encoders to replicate multimodal encoders using limited data. CSA maps unimodal features into a multimodal space, using a new similarity score to retain only the multimod… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.07610v3-abstract-full').style.display = 'inline'; document.getElementById('2410.07610v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.07610v3-abstract-full" style="display: none;"> Multimodal encoders like CLIP excel in tasks such as zero-shot image classification and cross-modal retrieval. However, they require excessive training data. We propose canonical similarity analysis (CSA), which uses two unimodal encoders to replicate multimodal encoders using limited data. CSA maps unimodal features into a multimodal space, using a new similarity score to retain only the multimodal information. CSA only involves the inference of unimodal encoders and a cubic-complexity matrix decomposition, eliminating the need for extensive GPU-based model training. Experiments show that CSA outperforms CLIP while requiring $50,000\times$ fewer multimodal data pairs to bridge the modalities given pre-trained unimodal encoders on ImageNet classification and misinformative news caption detection. CSA surpasses the state-of-the-art method to map unimodal features to multimodal features. We also demonstrate the ability of CSA with modalities beyond image and text, paving the way for future modality pairs with limited paired multimodal data but abundant unpaired unimodal data, such as lidar and text. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.07610v3-abstract-full').style.display = 'none'; document.getElementById('2410.07610v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 10 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> Published at ICLR 2025 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.05554">arXiv:2410.05554</a> <span> [<a href="https://arxiv.org/pdf/2410.05554">pdf</a>, <a href="https://arxiv.org/format/2410.05554">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> MultiNash-PF: A Particle Filtering Approach for Computing Multiple Local Generalized Nash Equilibria in Trajectory Games </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bhatt%2C+M">Maulik Bhatt</a>, <a href="/search/cs?searchtype=author&query=Askari%2C+I">Iman Askari</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+Y">Yue Yu</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a>, <a href="/search/cs?searchtype=author&query=Fang%2C+H">Huazhen Fang</a>, <a href="/search/cs?searchtype=author&query=Mehr%2C+N">Negar Mehr</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.05554v1-abstract-short" style="display: inline;"> Modern-world robotics involves complex environments where multiple autonomous agents must interact with each other and other humans. This necessitates advanced interactive multi-agent motion planning techniques. Generalized Nash equilibrium(GNE), a solution concept in constrained game theory, provides a mathematical model to predict the outcome of interactive motion planning, where each agent need… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.05554v1-abstract-full').style.display = 'inline'; document.getElementById('2410.05554v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.05554v1-abstract-full" style="display: none;"> Modern-world robotics involves complex environments where multiple autonomous agents must interact with each other and other humans. This necessitates advanced interactive multi-agent motion planning techniques. Generalized Nash equilibrium(GNE), a solution concept in constrained game theory, provides a mathematical model to predict the outcome of interactive motion planning, where each agent needs to account for other agents in the environment. However, in practice, multiple local GNEs may exist. Finding a single GNE itself is complex as it requires solving coupled constrained optimal control problems. Furthermore, finding all such local GNEs requires exploring the solution space of GNEs, which is a challenging task. This work proposes the MultiNash-PF framework to efficiently compute multiple local GNEs in constrained trajectory games. Potential games are a class of games for which a local GNE of a trajectory game can be found by solving a single constrained optimal control problem. We propose MultiNash-PF that integrates the potential game approach with implicit particle filtering, a sample-efficient method for non-convex trajectory optimization. We first formulate the underlying game as a constrained potential game and then utilize the implicit particle filtering to identify the coarse estimates of multiple local minimizers of the game's potential function. MultiNash-PF then refines these estimates with optimization solvers, obtaining different local GNEs. We show through numerical simulations that MultiNash-PF reduces computation time by up to 50\% compared to a baseline approach. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.05554v1-abstract-full').style.display = 'none'; document.getElementById('2410.05554v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.01144">arXiv:2410.01144</a> <span> [<a href="https://arxiv.org/pdf/2410.01144">pdf</a>, <a href="https://arxiv.org/format/2410.01144">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> </div> </div> <p class="title is-5 mathjax"> Uncertainty-Guided Enhancement on Driving Perception System via Foundation Models </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Yang%2C+Y">Yunhao Yang</a>, <a href="/search/cs?searchtype=author&query=Hu%2C+Y">Yuxin Hu</a>, <a href="/search/cs?searchtype=author&query=Ye%2C+M">Mao Ye</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Z">Zaiwei Zhang</a>, <a href="/search/cs?searchtype=author&query=Lu%2C+Z">Zhichao Lu</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+Y">Yi Xu</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a>, <a href="/search/cs?searchtype=author&query=Snyder%2C+B">Ben Snyder</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.01144v1-abstract-short" style="display: inline;"> Multimodal foundation models offer promising advancements for enhancing driving perception systems, but their high computational and financial costs pose challenges. We develop a method that leverages foundation models to refine predictions from existing driving perception models -- such as enhancing object classification accuracy -- while minimizing the frequency of using these resource-intensive… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.01144v1-abstract-full').style.display = 'inline'; document.getElementById('2410.01144v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.01144v1-abstract-full" style="display: none;"> Multimodal foundation models offer promising advancements for enhancing driving perception systems, but their high computational and financial costs pose challenges. We develop a method that leverages foundation models to refine predictions from existing driving perception models -- such as enhancing object classification accuracy -- while minimizing the frequency of using these resource-intensive models. The method quantitatively characterizes uncertainties in the perception model's predictions and engages the foundation model only when these uncertainties exceed a pre-specified threshold. Specifically, it characterizes uncertainty by calibrating the perception model's confidence scores into theoretical lower bounds on the probability of correct predictions using conformal prediction. Then, it sends images to the foundation model and queries for refining the predictions only if the theoretical bound of the perception model's outcome is below the threshold. Additionally, we propose a temporal inference mechanism that enhances prediction accuracy by integrating historical predictions, leading to tighter theoretical bounds. The method demonstrates a 10 to 15 percent improvement in prediction accuracy and reduces the number of queries to the foundation model by 50 percent, based on quantitative evaluations from driving datasets. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.01144v1-abstract-full').style.display = 'none'; document.getElementById('2410.01144v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2410.00171">arXiv:2410.00171</a> <span> [<a href="https://arxiv.org/pdf/2410.00171">pdf</a>, <a href="https://arxiv.org/format/2410.00171">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Basis-to-Basis Operator Learning Using Function Encoders </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ingebrand%2C+T">Tyler Ingebrand</a>, <a href="/search/cs?searchtype=author&query=Thorpe%2C+A+J">Adam J. Thorpe</a>, <a href="/search/cs?searchtype=author&query=Goswami%2C+S">Somdatta Goswami</a>, <a href="/search/cs?searchtype=author&query=Kumar%2C+K">Krishna Kumar</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2410.00171v2-abstract-short" style="display: inline;"> We present Basis-to-Basis (B2B) operator learning, a novel approach for learning operators on Hilbert spaces of functions based on the foundational ideas of function encoders. We decompose the task of learning operators into two parts: learning sets of basis functions for both the input and output spaces and learning a potentially nonlinear mapping between the coefficients of the basis functions.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.00171v2-abstract-full').style.display = 'inline'; document.getElementById('2410.00171v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2410.00171v2-abstract-full" style="display: none;"> We present Basis-to-Basis (B2B) operator learning, a novel approach for learning operators on Hilbert spaces of functions based on the foundational ideas of function encoders. We decompose the task of learning operators into two parts: learning sets of basis functions for both the input and output spaces and learning a potentially nonlinear mapping between the coefficients of the basis functions. B2B operator learning circumvents many challenges of prior works, such as requiring data to be at fixed locations, by leveraging classic techniques such as least squares to compute the coefficients. It is especially potent for linear operators, where we compute a mapping between bases as a single matrix transformation with a closed-form solution. Furthermore, with minimal modifications and using the deep theoretical connections between function encoders and functional analysis, we derive operator learning algorithms that are directly analogous to eigen-decomposition and singular value decomposition. We empirically validate B2B operator learning on seven benchmark operator learning tasks and show that it demonstrates a two-orders-of-magnitude improvement in accuracy over existing approaches on several benchmark tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2410.00171v2-abstract-full').style.display = 'none'; document.getElementById('2410.00171v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 30 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.19924">arXiv:2409.19924</a> <span> [<a href="https://arxiv.org/pdf/2409.19924">pdf</a>, <a href="https://arxiv.org/format/2409.19924">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> On The Planning Abilities of OpenAI's o1 Models: Feasibility, Optimality, and Generalizability </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Wang%2C+K">Kevin Wang</a>, <a href="/search/cs?searchtype=author&query=Li%2C+J">Junbo Li</a>, <a href="/search/cs?searchtype=author&query=Bhatt%2C+N+P">Neel P. Bhatt</a>, <a href="/search/cs?searchtype=author&query=Xi%2C+Y">Yihan Xi</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+Q">Qiang Liu</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+Z">Zhangyang Wang</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.19924v4-abstract-short" style="display: inline;"> Recent advancements in Large Language Models (LLMs) have showcased their ability to perform complex reasoning tasks, but their effectiveness in planning remains underexplored. In this study, we evaluate the planning capabilities of OpenAI's o1 models across a variety of benchmark tasks, focusing on three key aspects: feasibility, optimality, and generalizability. Through empirical evaluations on c… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.19924v4-abstract-full').style.display = 'inline'; document.getElementById('2409.19924v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.19924v4-abstract-full" style="display: none;"> Recent advancements in Large Language Models (LLMs) have showcased their ability to perform complex reasoning tasks, but their effectiveness in planning remains underexplored. In this study, we evaluate the planning capabilities of OpenAI's o1 models across a variety of benchmark tasks, focusing on three key aspects: feasibility, optimality, and generalizability. Through empirical evaluations on constraint-heavy tasks (e.g., $\textit{Barman}$, $\textit{Tyreworld}$) and spatially complex environments (e.g., $\textit{Termes}$, $\textit{Floortile}$), we highlight o1-preview's strengths in self-evaluation and constraint-following, while also identifying bottlenecks in decision-making and memory management, particularly in tasks requiring robust spatial reasoning. Our results reveal that o1-preview outperforms GPT-4 in adhering to task constraints and managing state transitions in structured environments. However, the model often generates suboptimal solutions with redundant actions and struggles to generalize effectively in spatially complex tasks. This pilot study provides foundational insights into the planning limitations of LLMs, offering key directions for future research on improving memory management, decision-making, and generalization in LLM-based planning. Code available at https://github.com/VITA-Group/o1-planning. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.19924v4-abstract-full').style.display = 'none'; document.getElementById('2409.19924v4-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 29 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Code available at https://github.com/VITA-Group/o1-planning</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.12397">arXiv:2409.12397</a> <span> [<a href="https://arxiv.org/pdf/2409.12397">pdf</a>, <a href="https://arxiv.org/format/2409.12397">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Learning to Coordinate without Communication under Incomplete Information </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chen%2C+S">Shenghui Chen</a>, <a href="/search/cs?searchtype=author&query=Zhu%2C+S">Shufang Zhu</a>, <a href="/search/cs?searchtype=author&query=De+Giacomo%2C+G">Giuseppe De Giacomo</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.12397v2-abstract-short" style="display: inline;"> Achieving seamless coordination in cooperative games is a crucial challenge in artificial intelligence, particularly when players operate under incomplete information. A common strategy to mitigate this information asymmetry involves leveraging explicit communication. However, direct (verbal) communication is not always feasible due to factors such as transmission loss. Leveraging the game Gnomes… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.12397v2-abstract-full').style.display = 'inline'; document.getElementById('2409.12397v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.12397v2-abstract-full" style="display: none;"> Achieving seamless coordination in cooperative games is a crucial challenge in artificial intelligence, particularly when players operate under incomplete information. A common strategy to mitigate this information asymmetry involves leveraging explicit communication. However, direct (verbal) communication is not always feasible due to factors such as transmission loss. Leveraging the game Gnomes at Night, we explore how effective coordination can be achieved without verbal communication, relying solely on observing each other's actions. We demonstrate how an autonomous agent can learn to cooperate by interpreting its partner's sequences of actions, which are used to hint at its intents. Our approach generates a non-Markovian strategy for the agent by learning a deterministic finite automaton for each possible action and integrating these automata into a finite-state transducer. Experimental results in a Gnomes at Night testbed show that, even without direct communication, one can learn effective cooperation strategies. Such strategies achieve significantly higher success rates and require fewer steps to complete the game compared to uncoordinated ones, and perform almost as well as in the case direct communication is allowed. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.12397v2-abstract-full').style.display = 'none'; document.getElementById('2409.12397v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 5 February, 2025; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 18 September, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2409.00015">arXiv:2409.00015</a> <span> [<a href="https://arxiv.org/pdf/2409.00015">pdf</a>, <a href="https://arxiv.org/format/2409.00015">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Navigating the sociotechnical labyrinth: Dynamic certification for responsible embodied AI </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bakirtzis%2C+G">Georgios Bakirtzis</a>, <a href="/search/cs?searchtype=author&query=Tubella%2C+A+A">Andrea Aler Tubella</a>, <a href="/search/cs?searchtype=author&query=Theodorou%2C+A">Andreas Theodorou</a>, <a href="/search/cs?searchtype=author&query=Danks%2C+D">David Danks</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2409.00015v1-abstract-short" style="display: inline;"> Sociotechnical requirements shape the governance of artificially intelligent (AI) systems. In an era where embodied AI technologies are rapidly reshaping various facets of contemporary society, their inherent dynamic adaptability presents a unique blend of opportunities and challenges. Traditional regulatory mechanisms, often designed for static -- or slower-paced -- technologies, find themselves… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.00015v1-abstract-full').style.display = 'inline'; document.getElementById('2409.00015v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.00015v1-abstract-full" style="display: none;"> Sociotechnical requirements shape the governance of artificially intelligent (AI) systems. In an era where embodied AI technologies are rapidly reshaping various facets of contemporary society, their inherent dynamic adaptability presents a unique blend of opportunities and challenges. Traditional regulatory mechanisms, often designed for static -- or slower-paced -- technologies, find themselves at a crossroads when faced with the fluid and evolving nature of AI systems. Moreover, typical problems in AI, for example, the frequent opacity and unpredictability of the behaviour of the systems, add additional sociotechnical challenges. To address these interconnected issues, we introduce the concept of dynamic certification, an adaptive regulatory framework specifically crafted to keep pace with the continuous evolution of AI systems. The complexity of these challenges requires common progress in multiple domains: technical, socio-governmental, and regulatory. Our proposed transdisciplinary approach is designed to ensure the safe, ethical, and practical deployment of AI systems, aligning them bidirectionally with the real-world contexts in which they operate. By doing so, we aim to bridge the gap between rapid technological advancement and effective regulatory oversight, ensuring that AI systems not only achieve their intended goals but also adhere to ethical standards and societal values. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.00015v1-abstract-full').style.display = 'none'; document.getElementById('2409.00015v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 16 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.13376">arXiv:2408.13376</a> <span> [<a href="https://arxiv.org/pdf/2408.13376">pdf</a>, <a href="https://arxiv.org/format/2408.13376">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Category Theory">math.CT</span> </div> </div> <p class="title is-5 mathjax"> Reduce, Reuse, Recycle: Categories for Compositional Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bakirtzis%2C+G">Georgios Bakirtzis</a>, <a href="/search/cs?searchtype=author&query=Savvas%2C+M">Michail Savvas</a>, <a href="/search/cs?searchtype=author&query=Zhao%2C+R">Ruihan Zhao</a>, <a href="/search/cs?searchtype=author&query=Chinchali%2C+S">Sandeep Chinchali</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.13376v2-abstract-short" style="display: inline;"> In reinforcement learning, conducting task composition by forming cohesive, executable sequences from multiple tasks remains challenging. However, the ability to (de)compose tasks is a linchpin in developing robotic systems capable of learning complex behaviors. Yet, compositional reinforcement learning is beset with difficulties, including the high dimensionality of the problem space, scarcity of… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.13376v2-abstract-full').style.display = 'inline'; document.getElementById('2408.13376v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.13376v2-abstract-full" style="display: none;"> In reinforcement learning, conducting task composition by forming cohesive, executable sequences from multiple tasks remains challenging. However, the ability to (de)compose tasks is a linchpin in developing robotic systems capable of learning complex behaviors. Yet, compositional reinforcement learning is beset with difficulties, including the high dimensionality of the problem space, scarcity of rewards, and absence of system robustness after task composition. To surmount these challenges, we view task composition through the prism of category theory -- a mathematical discipline exploring structures and their compositional relationships. The categorical properties of Markov decision processes untangle complex tasks into manageable sub-tasks, allowing for strategical reduction of dimensionality, facilitating more tractable reward structures, and bolstering system robustness. Experimental results support the categorical theory of reinforcement learning by enabling skill reduction, reuse, and recycling when learning complex robotic arm tasks. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.13376v2-abstract-full').style.display = 'none'; document.getElementById('2408.13376v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 4 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 23 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">ECAI 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.11186">arXiv:2408.11186</a> <span> [<a href="https://arxiv.org/pdf/2408.11186">pdf</a>, <a href="https://arxiv.org/ps/2408.11186">ps</a>, <a href="https://arxiv.org/format/2408.11186">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Optimization and Control">math.OC</span> </div> </div> <p class="title is-5 mathjax"> Sequential Resource Trading Using Comparison-Based Gradient Estimation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Murthy%2C+S">Surya Murthy</a>, <a href="/search/cs?searchtype=author&query=Karabag%2C+M+O">Mustafa O. Karabag</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.11186v2-abstract-short" style="display: inline;"> Autonomous agents interact with other agents of unknown preferences to share resources in their environment. We explore sequential trading for resource allocation in a setting where two greedily rational agents sequentially trade resources from a finite set of categories. Each agent has a utility function that depends on the amount of resources it possesses in each category. The offering agent mak… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.11186v2-abstract-full').style.display = 'inline'; document.getElementById('2408.11186v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.11186v2-abstract-full" style="display: none;"> Autonomous agents interact with other agents of unknown preferences to share resources in their environment. We explore sequential trading for resource allocation in a setting where two greedily rational agents sequentially trade resources from a finite set of categories. Each agent has a utility function that depends on the amount of resources it possesses in each category. The offering agent makes trade offers to improve its utility without knowing the responding agent's utility function, and the responding agent only accepts offers that improve its utility. We present an algorithm for the offering agent to estimate the responding agent's gradient (preferences) and make offers based on previous acceptance or rejection responses. The algorithm's goal is to reach a Pareto-optimal resource allocation state while ensuring that the utilities of both agents improve after every accepted trade. We show that, after a finite number of consecutively rejected offers, the responding agent is at a near-optimal state, or the agents' gradients are closely aligned. We compare the proposed algorithm against various baselines in continuous and discrete trading scenarios and show that it improves the societal benefit with fewer offers. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.11186v2-abstract-full').style.display = 'none'; document.getElementById('2408.11186v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.08770">arXiv:2408.08770</a> <span> [<a href="https://arxiv.org/pdf/2408.08770">pdf</a>, <a href="https://arxiv.org/format/2408.08770">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Pessimistic Iterative Planning for Robust POMDPs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Galesloot%2C+M+F+L">Maris F. L. Galesloot</a>, <a href="/search/cs?searchtype=author&query=Suilen%2C+M">Marnix Suilen</a>, <a href="/search/cs?searchtype=author&query=Sim%C3%A3o%2C+T+D">Thiago D. Sim茫o</a>, <a href="/search/cs?searchtype=author&query=Carr%2C+S">Steven Carr</a>, <a href="/search/cs?searchtype=author&query=Spaan%2C+M+T+J">Matthijs T. J. Spaan</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a>, <a href="/search/cs?searchtype=author&query=Jansen%2C+N">Nils Jansen</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.08770v3-abstract-short" style="display: inline;"> Robust POMDPs extend classical POMDPs to handle model uncertainty. Specifically, robust POMDPs exhibit so-called uncertainty sets on the transition and observation models, effectively defining ranges of probabilities. Policies for robust POMDPs must be (1) memory-based to account for partial observability and (2) robust against model uncertainty to account for the worst-case instances from the unc… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.08770v3-abstract-full').style.display = 'inline'; document.getElementById('2408.08770v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.08770v3-abstract-full" style="display: none;"> Robust POMDPs extend classical POMDPs to handle model uncertainty. Specifically, robust POMDPs exhibit so-called uncertainty sets on the transition and observation models, effectively defining ranges of probabilities. Policies for robust POMDPs must be (1) memory-based to account for partial observability and (2) robust against model uncertainty to account for the worst-case instances from the uncertainty sets. To compute such robust memory-based policies, we propose the pessimistic iterative planning (PIP) framework, which alternates between two main steps: (1) selecting a pessimistic (non-robust) POMDP via worst-case probability instances from the uncertainty sets; and (2) computing a finite-state controller (FSC) for this pessimistic POMDP. We evaluate the performance of this FSC on the original robust POMDP and use this evaluation in step (1) to select the next pessimistic POMDP. Within PIP, we propose the rFSCNet algorithm. In each iteration, rFSCNet finds an FSC through a recurrent neural network by using supervision policies optimized for the pessimistic POMDP. The empirical evaluation in four benchmark environments showcases improved robustness against several baseline methods and competitive performance compared to a state-of-the-art robust POMDP solver. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.08770v3-abstract-full').style.display = 'none'; document.getElementById('2408.08770v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 November, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 16 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.06431">arXiv:2408.06431</a> <span> [<a href="https://arxiv.org/pdf/2408.06431">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> </div> </div> <p class="title is-5 mathjax"> Addressing the Unforeseen Harms of Technology CCC Whitepaper </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bliss%2C+N">Nadya Bliss</a>, <a href="/search/cs?searchtype=author&query=Butler%2C+K">Kevin Butler</a>, <a href="/search/cs?searchtype=author&query=Danks%2C+D">David Danks</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a>, <a href="/search/cs?searchtype=author&query=Turk%2C+M">Matthew Turk</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.06431v1-abstract-short" style="display: inline;"> Recent years have seen increased awareness of the potential significant impacts of computing technologies, both positive and negative. This whitepaper explores how to address possible harmful consequences of computing technologies that might be difficult to anticipate, and thereby mitigate or address. It starts from the assumption that very few harms due to technology are intentional or deliberate… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.06431v1-abstract-full').style.display = 'inline'; document.getElementById('2408.06431v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.06431v1-abstract-full" style="display: none;"> Recent years have seen increased awareness of the potential significant impacts of computing technologies, both positive and negative. This whitepaper explores how to address possible harmful consequences of computing technologies that might be difficult to anticipate, and thereby mitigate or address. It starts from the assumption that very few harms due to technology are intentional or deliberate; rather, the vast majority result from failure to recognize and respond to them prior to deployment. Nonetheless, there are concrete steps that can be taken to address the difficult problem of anticipating and responding to potential harms from new technologies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.06431v1-abstract-full').style.display = 'none'; document.getElementById('2408.06431v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2408.02860">arXiv:2408.02860</a> <span> [<a href="https://arxiv.org/pdf/2408.02860">pdf</a>, <a href="https://arxiv.org/format/2408.02860">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Science and Game Theory">cs.GT</span> </div> </div> <p class="title is-5 mathjax"> Nash Equilibrium in Games on Graphs with Incomplete Preferences </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Kulkarni%2C+A+N">Abhishek N. Kulkarni</a>, <a href="/search/cs?searchtype=author&query=Fu%2C+J">Jie Fu</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2408.02860v2-abstract-short" style="display: inline;"> Games with incomplete preferences are an important model for studying rational decision-making in scenarios where players face incomplete information about their preferences and must contend with incomparable outcomes. We study the problem of computing Nash equilibrium in a subclass of two-player games played on graphs where each player seeks to maximally satisfy their (possibly incomplete) prefer… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.02860v2-abstract-full').style.display = 'inline'; document.getElementById('2408.02860v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2408.02860v2-abstract-full" style="display: none;"> Games with incomplete preferences are an important model for studying rational decision-making in scenarios where players face incomplete information about their preferences and must contend with incomparable outcomes. We study the problem of computing Nash equilibrium in a subclass of two-player games played on graphs where each player seeks to maximally satisfy their (possibly incomplete) preferences over a set of temporal goals. We characterize the Nash equilibrium and prove its existence in scenarios where player preferences are fully aligned, partially aligned, and completely opposite, in terms of the well-known solution concepts of sure winning and Pareto efficiency. When preferences are partially aligned, we derive conditions under which a player needs cooperation and demonstrate that the Nash equilibria depend not only on the preference alignment but also on whether the players need cooperation to achieve a better outcome and whether they are willing to cooperate.We illustrate the theoretical results by solving a mechanism design problem for a drone delivery scenario. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2408.02860v2-abstract-full').style.display = 'none'; document.getElementById('2408.02860v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 August, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">14 page, 6 figure, under development</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.07556">arXiv:2406.07556</a> <span> [<a href="https://arxiv.org/pdf/2406.07556">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computers and Society">cs.CY</span> </div> </div> <p class="title is-5 mathjax"> Community Driven Approaches to Research in Technology & Society CCC Workshop Report </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Venkatasubramanian%2C+S">Suresh Venkatasubramanian</a>, <a href="/search/cs?searchtype=author&query=Gebru%2C+T">Timnit Gebru</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a>, <a href="/search/cs?searchtype=author&query=Griffin%2C+H">Haley Griffin</a>, <a href="/search/cs?searchtype=author&query=Rosenbloom%2C+L+N">Leah Namisa Rosenbloom</a>, <a href="/search/cs?searchtype=author&query=Sonboli%2C+N">Nasim Sonboli</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.07556v1-abstract-short" style="display: inline;"> Based on our workshop activities, we outlined three ways in which research can support community needs: (1) Mapping the ecosystem of both the players and ecosystem and harm landscapes, (2) Counter-Programming, which entails using the same surveillance tools that communities are subjected to observe the entities doing the surveilling, effectively protecting people from surveillance, and conducting… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.07556v1-abstract-full').style.display = 'inline'; document.getElementById('2406.07556v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.07556v1-abstract-full" style="display: none;"> Based on our workshop activities, we outlined three ways in which research can support community needs: (1) Mapping the ecosystem of both the players and ecosystem and harm landscapes, (2) Counter-Programming, which entails using the same surveillance tools that communities are subjected to observe the entities doing the surveilling, effectively protecting people from surveillance, and conducting ethical data collection to measure the impact of these technologies, and (3) Engaging in positive visions and tools for empowerment so that technology can bring good instead of harm. In order to effectively collaborate on the aforementioned directions, we outlined seven important mechanisms for effective collaboration: (1) Never expect free labor of community members, (2) Ensure goals are aligned between all collaborators, (3) Elevate community members to leadership positions, (4) Understand no group is a monolith, (5) Establish a common language, (6) Discuss organization roles and goals of the project transparently from the start, and (7) Enable a recourse for harm. We recommend that anyone engaging in community-based research (1) starts with community-defined solutions, (2) provides alternatives to digital services/information collecting mechanisms, (3) prohibits harmful automated systems, (4) transparently states any systems impact, (5) minimizes and protects data, (6) proactively demonstrates a system is safe and beneficial prior to deployment, and (7) provides resources directly to community partners. Throughout the recommendation section of the report, we also provide specific recommendations for funding agencies, academic institutions, and individual researchers. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.07556v1-abstract-full').style.display = 'none'; document.getElementById('2406.07556v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 21 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2406.03565">arXiv:2406.03565</a> <span> [<a href="https://arxiv.org/pdf/2406.03565">pdf</a>, <a href="https://arxiv.org/format/2406.03565">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Science and Game Theory">cs.GT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Second-Order Algorithms for Finding Local Nash Equilibria in Zero-Sum Games </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gupta%2C+K">Kushagra Gupta</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+X">Xinjie Liu</a>, <a href="/search/cs?searchtype=author&query=Allen%2C+R">Ross Allen</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a>, <a href="/search/cs?searchtype=author&query=Fridovich-Keil%2C+D">David Fridovich-Keil</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2406.03565v2-abstract-short" style="display: inline;"> Zero-sum games arise in a wide variety of problems, including robust optimization and adversarial learning. However, algorithms deployed for finding a local Nash equilibrium in these games often converge to non-Nash stationary points. This highlights a key challenge: for any algorithm, the stability properties of its underlying dynamical system can cause non-Nash points to be potential attractors.… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.03565v2-abstract-full').style.display = 'inline'; document.getElementById('2406.03565v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2406.03565v2-abstract-full" style="display: none;"> Zero-sum games arise in a wide variety of problems, including robust optimization and adversarial learning. However, algorithms deployed for finding a local Nash equilibrium in these games often converge to non-Nash stationary points. This highlights a key challenge: for any algorithm, the stability properties of its underlying dynamical system can cause non-Nash points to be potential attractors. To overcome this challenge, algorithms must account for subtleties involving the curvatures of players' costs. To this end, we leverage dynamical system theory and develop a second-order algorithm for finding a local Nash equilibrium in the smooth, possibly nonconvex-nonconcave, zero-sum game setting. First, we prove that this novel method guarantees convergence to only local Nash equilibria with a local linear convergence rate. We then interpret a version of this method as a modified Gauss-Newton algorithm with local superlinear convergence to the neighborhood of a point that satisfies first-order local Nash equilibrium conditions. In comparison, current related state-of-the-art methods do not offer convergence rate guarantees. Furthermore, we show that this approach naturally generalizes to settings with convex and potentially coupled constraints while retaining earlier guarantees of convergence to only local (generalized) Nash equilibria. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2406.03565v2-abstract-full').style.display = 'none'; document.getElementById('2406.03565v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 3 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 5 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.14173">arXiv:2405.14173</a> <span> [<a href="https://arxiv.org/pdf/2405.14173">pdf</a>, <a href="https://arxiv.org/format/2405.14173">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Human-Computer Interaction">cs.HC</span> </div> </div> <p class="title is-5 mathjax"> Human-Agent Cooperation in Games under Incomplete Information through Natural Language Communication </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Chen%2C+S">Shenghui Chen</a>, <a href="/search/cs?searchtype=author&query=Fried%2C+D">Daniel Fried</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.14173v3-abstract-short" style="display: inline;"> Developing autonomous agents that can strategize and cooperate with humans under information asymmetry is challenging without effective communication in natural language. We introduce a shared-control game, where two players collectively control a token in alternating turns to achieve a common objective under incomplete information. We formulate a policy synthesis problem for an autonomous agent i… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.14173v3-abstract-full').style.display = 'inline'; document.getElementById('2405.14173v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.14173v3-abstract-full" style="display: none;"> Developing autonomous agents that can strategize and cooperate with humans under information asymmetry is challenging without effective communication in natural language. We introduce a shared-control game, where two players collectively control a token in alternating turns to achieve a common objective under incomplete information. We formulate a policy synthesis problem for an autonomous agent in this game with a human as the other player. To solve this problem, we propose a communication-based approach comprising a language module and a planning module. The language module translates natural language messages into and from a finite set of flags, a compact representation defined to capture player intents. The planning module leverages these flags to compute a policy using an asymmetric information-set Monte Carlo tree search with flag exchange algorithm we present. We evaluate the effectiveness of this approach in a testbed based on Gnomes at Night, a search-and-find maze board game. Results of human subject experiments show that communication narrows the information gap between players and enhances human-agent cooperation efficiency with fewer turns. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.14173v3-abstract-full').style.display = 'none'; document.getElementById('2405.14173v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 23 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">with appendix</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2405.08954">arXiv:2405.08954</a> <span> [<a href="https://arxiv.org/pdf/2405.08954">pdf</a>, <a href="https://arxiv.org/format/2405.08954">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Zero-Shot Transfer of Neural ODEs </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ingebrand%2C+T">Tyler Ingebrand</a>, <a href="/search/cs?searchtype=author&query=Thorpe%2C+A+J">Adam J. Thorpe</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2405.08954v2-abstract-short" style="display: inline;"> Autonomous systems often encounter environments and scenarios beyond the scope of their training data, which underscores a critical challenge: the need to generalize and adapt to unseen scenarios in real time. This challenge necessitates new mathematical and algorithmic tools that enable adaptation and zero-shot transfer. To this end, we leverage the theory of function encoders, which enables zero… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.08954v2-abstract-full').style.display = 'inline'; document.getElementById('2405.08954v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2405.08954v2-abstract-full" style="display: none;"> Autonomous systems often encounter environments and scenarios beyond the scope of their training data, which underscores a critical challenge: the need to generalize and adapt to unseen scenarios in real time. This challenge necessitates new mathematical and algorithmic tools that enable adaptation and zero-shot transfer. To this end, we leverage the theory of function encoders, which enables zero-shot transfer by combining the flexibility of neural networks with the mathematical principles of Hilbert spaces. Using this theory, we first present a method for learning a space of dynamics spanned by a set of neural ODE basis functions. After training, the proposed approach can rapidly identify dynamics in the learned space using an efficient inner product calculation. Critically, this calculation requires no gradient calculations or retraining during the online phase. This method enables zero-shot transfer for autonomous systems at runtime and opens the door for a new class of adaptable control algorithms. We demonstrate state-of-the-art system modeling accuracy for two MuJoCo robot environments and show that the learned models can be used for more efficient MPC control of a quadrotor. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2405.08954v2-abstract-full').style.display = 'none'; document.getElementById('2405.08954v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 14 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> May 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2404.00923">arXiv:2404.00923</a> <span> [<a href="https://arxiv.org/pdf/2404.00923">pdf</a>, <a href="https://arxiv.org/format/2404.00923">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Vision and Pattern Recognition">cs.CV</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> MM3DGS SLAM: Multi-modal 3D Gaussian Splatting for SLAM Using Vision, Depth, and Inertial Measurements </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Sun%2C+L+C">Lisong C. Sun</a>, <a href="/search/cs?searchtype=author&query=Bhatt%2C+N+P">Neel P. Bhatt</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+J+C">Jonathan C. Liu</a>, <a href="/search/cs?searchtype=author&query=Fan%2C+Z">Zhiwen Fan</a>, <a href="/search/cs?searchtype=author&query=Wang%2C+Z">Zhangyang Wang</a>, <a href="/search/cs?searchtype=author&query=Humphreys%2C+T+E">Todd E. Humphreys</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2404.00923v1-abstract-short" style="display: inline;"> Simultaneous localization and mapping is essential for position tracking and scene understanding. 3D Gaussian-based map representations enable photorealistic reconstruction and real-time rendering of scenes using multiple posed cameras. We show for the first time that using 3D Gaussians for map representation with unposed camera images and inertial measurements can enable accurate SLAM. Our method… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.00923v1-abstract-full').style.display = 'inline'; document.getElementById('2404.00923v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2404.00923v1-abstract-full" style="display: none;"> Simultaneous localization and mapping is essential for position tracking and scene understanding. 3D Gaussian-based map representations enable photorealistic reconstruction and real-time rendering of scenes using multiple posed cameras. We show for the first time that using 3D Gaussians for map representation with unposed camera images and inertial measurements can enable accurate SLAM. Our method, MM3DGS, addresses the limitations of prior neural radiance field-based representations by enabling faster rendering, scale awareness, and improved trajectory tracking. Our framework enables keyframe-based mapping and tracking utilizing loss functions that incorporate relative pose transformations from pre-integrated inertial measurements, depth estimates, and measures of photometric rendering quality. We also release a multi-modal dataset, UT-MM, collected from a mobile robot equipped with a camera and an inertial measurement unit. Experimental evaluation on several scenes from the dataset shows that MM3DGS achieves 3x improvement in tracking and 5% improvement in photometric rendering quality compared to the current 3DGS SLAM state-of-the-art, while allowing real-time rendering of a high-resolution dense 3D map. Project Webpage: https://vita-group.github.io/MM3DGS-SLAM <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2404.00923v1-abstract-full').style.display = 'none'; document.getElementById('2404.00923v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 1 April, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> April 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Project Webpage: https://vita-group.github.io/MM3DGS-SLAM</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.17233">arXiv:2403.17233</a> <span> [<a href="https://arxiv.org/pdf/2403.17233">pdf</a>, <a href="https://arxiv.org/format/2403.17233">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Active Learning of Dynamics Using Prior Domain Knowledge in the Sampling Process </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Miller%2C+K+S">Kevin S. Miller</a>, <a href="/search/cs?searchtype=author&query=Thorpe%2C+A+J">Adam J. Thorpe</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.17233v1-abstract-short" style="display: inline;"> We present an active learning algorithm for learning dynamics that leverages side information by explicitly incorporating prior domain knowledge into the sampling process. Our proposed algorithm guides the exploration toward regions that demonstrate high empirical discrepancy between the observed data and an imperfect prior model of the dynamics derived from side information. Through numerical exp… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.17233v1-abstract-full').style.display = 'inline'; document.getElementById('2403.17233v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.17233v1-abstract-full" style="display: none;"> We present an active learning algorithm for learning dynamics that leverages side information by explicitly incorporating prior domain knowledge into the sampling process. Our proposed algorithm guides the exploration toward regions that demonstrate high empirical discrepancy between the observed data and an imperfect prior model of the dynamics derived from side information. Through numerical experiments, we demonstrate that this strategy explores regions of high discrepancy and accelerates learning while simultaneously reducing model uncertainty. We rigorously prove that our active learning algorithm yields a consistent estimate of the underlying dynamics by providing an explicit rate of convergence for the maximum predictive variance. We demonstrate the efficacy of our approach on an under-actuated pendulum system and on the half-cheetah MuJoCo environment. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.17233v1-abstract-full').style.display = 'none'; document.getElementById('2403.17233v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 25 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.12279">arXiv:2403.12279</a> <span> [<a href="https://arxiv.org/pdf/2403.12279">pdf</a>, <a href="https://arxiv.org/format/2403.12279">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> </div> </div> <p class="title is-5 mathjax"> Scalable Networked Feature Selection with Randomized Algorithm for Robot Navigation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Pandey%2C+V">Vivek Pandey</a>, <a href="/search/cs?searchtype=author&query=Amini%2C+A">Arash Amini</a>, <a href="/search/cs?searchtype=author&query=Liu%2C+G">Guangyi Liu</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a>, <a href="/search/cs?searchtype=author&query=Sun%2C+Q">Qiyu Sun</a>, <a href="/search/cs?searchtype=author&query=Daniilidis%2C+K">Kostas Daniilidis</a>, <a href="/search/cs?searchtype=author&query=Motee%2C+N">Nader Motee</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.12279v1-abstract-short" style="display: inline;"> We address the problem of sparse selection of visual features for localizing a team of robots navigating an unknown environment, where robots can exchange relative position measurements with neighbors. We select a set of the most informative features by anticipating their importance in robots localization by simulating trajectories of robots over a prediction horizon. Through theoretical proofs, w… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.12279v1-abstract-full').style.display = 'inline'; document.getElementById('2403.12279v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.12279v1-abstract-full" style="display: none;"> We address the problem of sparse selection of visual features for localizing a team of robots navigating an unknown environment, where robots can exchange relative position measurements with neighbors. We select a set of the most informative features by anticipating their importance in robots localization by simulating trajectories of robots over a prediction horizon. Through theoretical proofs, we establish a crucial connection between graph Laplacian and the importance of features. We show that strong network connectivity translates to uniformity in feature importance, which enables uniform random sampling of features and reduces the overall computational complexity. We leverage a scalable randomized algorithm for sparse sums of positive semidefinite matrices to efficiently select the set of the most informative features and significantly improve the probabilistic performance bounds. Finally, we support our findings with extensive simulations. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.12279v1-abstract-full').style.display = 'none'; document.getElementById('2403.12279v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 18 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.10705">arXiv:2403.10705</a> <span> [<a href="https://arxiv.org/pdf/2403.10705">pdf</a>, <a href="https://arxiv.org/format/2403.10705">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> Susceptibility of Communities against Low-Credibility Content in Social News Websites </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bayiz%2C+Y+E">Yigit Ege Bayiz</a>, <a href="/search/cs?searchtype=author&query=Amini%2C+A">Arash Amini</a>, <a href="/search/cs?searchtype=author&query=Marculescu%2C+R">Radu Marculescu</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.10705v1-abstract-short" style="display: inline;"> Social news websites, such as Reddit, have evolved into prominent platforms for sharing and discussing news. A key issue on social news websites sites is the formation of echo chambers, which often lead to the spread of highly biased or uncredible news. We develop a method to identify communities within a social news website that are prone to uncredible or highly biased news. We employ a user embe… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.10705v1-abstract-full').style.display = 'inline'; document.getElementById('2403.10705v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.10705v1-abstract-full" style="display: none;"> Social news websites, such as Reddit, have evolved into prominent platforms for sharing and discussing news. A key issue on social news websites sites is the formation of echo chambers, which often lead to the spread of highly biased or uncredible news. We develop a method to identify communities within a social news website that are prone to uncredible or highly biased news. We employ a user embedding pipeline that detects user communities based on their stances towards posts and news sources. We then project each community onto a credibility-bias space and analyze the distributional characteristics of each projected community to identify those that have a high risk of adopting beliefs with low credibility or high bias. This approach also enables the prediction of individual users' susceptibility to low credibility content, based on their community affiliation. Our experiments show that latent space clusters effectively indicate the credibility and bias levels of their users, with significant differences observed across clusters -- a $34\%$ difference in the users' susceptibility to low-credibility content and a $8.3\%$ difference in the users' susceptibility to high political bias. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.10705v1-abstract-full').style.display = 'none'; document.getElementById('2403.10705v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">11 pages, 2 figures, Under review in ICWSM 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2403.10384">arXiv:2403.10384</a> <span> [<a href="https://arxiv.org/pdf/2403.10384">pdf</a>, <a href="https://arxiv.org/format/2403.10384">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computer Science and Game Theory">cs.GT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Coordination in Noncooperative Multiplayer Matrix Games via Reduced Rank Correlated Equilibria </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Im%2C+J">Jaehan Im</a>, <a href="/search/cs?searchtype=author&query=Yu%2C+Y">Yue Yu</a>, <a href="/search/cs?searchtype=author&query=Fridovich-Keil%2C+D">David Fridovich-Keil</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2403.10384v2-abstract-short" style="display: inline;"> Coordination in multiplayer games enables players to avoid the lose-lose outcome that often arises at Nash equilibria. However, designing a coordination mechanism typically requires the consideration of the joint actions of all players, which becomes intractable in large-scale games. We develop a novel coordination mechanism, termed reduced rank correlated equilibria, which reduces the number of j… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.10384v2-abstract-full').style.display = 'inline'; document.getElementById('2403.10384v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2403.10384v2-abstract-full" style="display: none;"> Coordination in multiplayer games enables players to avoid the lose-lose outcome that often arises at Nash equilibria. However, designing a coordination mechanism typically requires the consideration of the joint actions of all players, which becomes intractable in large-scale games. We develop a novel coordination mechanism, termed reduced rank correlated equilibria, which reduces the number of joint actions to be considered and thereby mitigates computational complexity. The idea is to approximate the set of all joint actions with the actions used in a set of pre-computed Nash equilibria via a convex hull operation. In a game with n players and each player having m actions, the proposed mechanism reduces the number of joint actions considered from O(m^n) to O(mn). We demonstrate the application of the proposed mechanism to an air traffic queue management problem. Compared with the correlated equilibrium-a popular benchmark coordination mechanism-the proposed approach is capable of solving a problem involving four thousand times more joint actions while yielding similar or better performance in terms of a fairness indicator and showing a maximum optimality gap of 0.066% in terms of the average delay cost. In the meantime, it yields a solution that shows up to 99.5% improvement in a fairness indicator and up to 50.4% reduction in average delay cost compared to the Nash solution, which does not involve coordination. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2403.10384v2-abstract-full').style.display = 'none'; document.getElementById('2403.10384v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 March, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.10938">arXiv:2402.10938</a> <span> [<a href="https://arxiv.org/pdf/2402.10938">pdf</a>, <a href="https://arxiv.org/format/2402.10938">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> </div> </div> <p class="title is-5 mathjax"> News Source Credibility Assessment: A Reddit Case Study </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Amini%2C+A">Arash Amini</a>, <a href="/search/cs?searchtype=author&query=Bayiz%2C+Y+E">Yigit Ege Bayiz</a>, <a href="/search/cs?searchtype=author&query=Ram%2C+A">Ashwin Ram</a>, <a href="/search/cs?searchtype=author&query=Marculescu%2C+R">Radu Marculescu</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.10938v1-abstract-short" style="display: inline;"> In the era of social media platforms, identifying the credibility of online content is crucial to combat misinformation. We present the CREDiBERT (CREDibility assessment using Bi-directional Encoder Representations from Transformers), a source credibility assessment model fine-tuned for Reddit submissions focusing on political discourse as the main contribution. We adopt a semi-supervised training… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.10938v1-abstract-full').style.display = 'inline'; document.getElementById('2402.10938v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.10938v1-abstract-full" style="display: none;"> In the era of social media platforms, identifying the credibility of online content is crucial to combat misinformation. We present the CREDiBERT (CREDibility assessment using Bi-directional Encoder Representations from Transformers), a source credibility assessment model fine-tuned for Reddit submissions focusing on political discourse as the main contribution. We adopt a semi-supervised training approach for CREDiBERT, leveraging Reddit's community-based structure. By encoding submission content using CREDiBERT and integrating it into a Siamese neural network, we significantly improve the binary classification of submission credibility, achieving a 9% increase in F1 score compared to existing methods. Additionally, we introduce a new version of the post-to-post network in Reddit that efficiently encodes user interactions to enhance the binary classification task by nearly 8% in F1 score. Finally, we employ CREDiBERT to evaluate the susceptibility of subreddits with respect to different topics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.10938v1-abstract-full').style.display = 'none'; document.getElementById('2402.10938v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 7 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">12 pages; 3 figures</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.08902">arXiv:2402.08902</a> <span> [<a href="https://arxiv.org/pdf/2402.08902">pdf</a>, <a href="https://arxiv.org/format/2402.08902">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computer Science and Game Theory">cs.GT</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Auto-Encoding Bayesian Inverse Games </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Liu%2C+X">Xinjie Liu</a>, <a href="/search/cs?searchtype=author&query=Peters%2C+L">Lasse Peters</a>, <a href="/search/cs?searchtype=author&query=Alonso-Mora%2C+J">Javier Alonso-Mora</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a>, <a href="/search/cs?searchtype=author&query=Fridovich-Keil%2C+D">David Fridovich-Keil</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.08902v3-abstract-short" style="display: inline;"> When multiple agents interact in a common environment, each agent's actions impact others' future decisions, and noncooperative dynamic games naturally capture this coupling. In interactive motion planning, however, agents typically do not have access to a complete model of the game, e.g., due to unknown objectives of other players. Therefore, we consider the inverse game problem, in which some pr… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.08902v3-abstract-full').style.display = 'inline'; document.getElementById('2402.08902v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.08902v3-abstract-full" style="display: none;"> When multiple agents interact in a common environment, each agent's actions impact others' future decisions, and noncooperative dynamic games naturally capture this coupling. In interactive motion planning, however, agents typically do not have access to a complete model of the game, e.g., due to unknown objectives of other players. Therefore, we consider the inverse game problem, in which some properties of the game are unknown a priori and must be inferred from observations. Existing maximum likelihood estimation (MLE) approaches to solve inverse games provide only point estimates of unknown parameters without quantifying uncertainty, and perform poorly when many parameter values explain the observed behavior. To address these limitations, we take a Bayesian perspective and construct posterior distributions of game parameters. To render inference tractable, we employ a variational autoencoder (VAE) with an embedded differentiable game solver. This structured VAE can be trained from an unlabeled dataset of observed interactions, naturally handles continuous, multi-modal distributions, and supports efficient sampling from the inferred posteriors without computing game solutions at runtime. Extensive evaluations in simulated driving scenarios demonstrate that the proposed approach successfully learns the prior and posterior game parameter distributions, provides more accurate objective estimates than MLE baselines, and facilitates safer and more efficient game-theoretic motion planning. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.08902v3-abstract-full').style.display = 'none'; document.getElementById('2402.08902v3-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 15 June, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 13 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> International Workshop on the Algorithmic Foundations of Robotics 2024 (WAFR) </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.08570">arXiv:2402.08570</a> <span> [<a href="https://arxiv.org/pdf/2402.08570">pdf</a>, <a href="https://arxiv.org/format/2402.08570">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Online Foundation Model Selection in Robotics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Li%2C+P">Po-han Li</a>, <a href="/search/cs?searchtype=author&query=Toprak%2C+O+S">Oyku Selin Toprak</a>, <a href="/search/cs?searchtype=author&query=Narayanan%2C+A">Aditya Narayanan</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a>, <a href="/search/cs?searchtype=author&query=Chinchali%2C+S">Sandeep Chinchali</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.08570v1-abstract-short" style="display: inline;"> Foundation models have recently expanded into robotics after excelling in computer vision and natural language processing. The models are accessible in two ways: open-source or paid, closed-source options. Users with access to both face a problem when deciding between effective yet costly closed-source models and free but less powerful open-source alternatives. We call it the model selection probl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.08570v1-abstract-full').style.display = 'inline'; document.getElementById('2402.08570v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.08570v1-abstract-full" style="display: none;"> Foundation models have recently expanded into robotics after excelling in computer vision and natural language processing. The models are accessible in two ways: open-source or paid, closed-source options. Users with access to both face a problem when deciding between effective yet costly closed-source models and free but less powerful open-source alternatives. We call it the model selection problem. Existing supervised-learning methods are impractical due to the high cost of collecting extensive training data from closed-source models. Hence, we focus on the online learning setting where algorithms learn while collecting data, eliminating the need for large pre-collected datasets. We thus formulate a user-centric online model selection problem and propose a novel solution that combines an open-source encoder to output context and an online learning algorithm that processes this context. The encoder distills vast data distributions into low-dimensional features, i.e., the context, without additional training. The online learning algorithm aims to maximize a composite reward that includes model performance, execution time, and costs based on the context extracted from the data. It results in an improved trade-off between selecting open-source and closed-source models compared to non-contextual methods, as validated by our theoretical analysis. Experiments across language-based robotic tasks such as Waymo Open Dataset, ALFRED, and Open X-Embodiment demonstrate real-world applications of the solution. The results show that the solution significantly improves the task success rate by up to 14%. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.08570v1-abstract-full').style.display = 'none'; document.getElementById('2402.08570v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 13 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2402.07069">arXiv:2402.07069</a> <span> [<a href="https://arxiv.org/pdf/2402.07069">pdf</a>, <a href="https://arxiv.org/format/2402.07069">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Computation and Language">cs.CL</span> </div> </div> <p class="title is-5 mathjax"> Using Large Language Models to Automate and Expedite Reinforcement Learning with Reward Machine </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Alsadat%2C+S+M">Shayan Meshkat Alsadat</a>, <a href="/search/cs?searchtype=author&query=Gaglione%2C+J">Jean-Raphael Gaglione</a>, <a href="/search/cs?searchtype=author&query=Neider%2C+D">Daniel Neider</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a>, <a href="/search/cs?searchtype=author&query=Xu%2C+Z">Zhe Xu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2402.07069v1-abstract-short" style="display: inline;"> We present LARL-RM (Large language model-generated Automaton for Reinforcement Learning with Reward Machine) algorithm in order to encode high-level knowledge into reinforcement learning using automaton to expedite the reinforcement learning. Our method uses Large Language Models (LLM) to obtain high-level domain-specific knowledge using prompt engineering instead of providing the reinforcement le… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.07069v1-abstract-full').style.display = 'inline'; document.getElementById('2402.07069v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2402.07069v1-abstract-full" style="display: none;"> We present LARL-RM (Large language model-generated Automaton for Reinforcement Learning with Reward Machine) algorithm in order to encode high-level knowledge into reinforcement learning using automaton to expedite the reinforcement learning. Our method uses Large Language Models (LLM) to obtain high-level domain-specific knowledge using prompt engineering instead of providing the reinforcement learning algorithm directly with the high-level knowledge which requires an expert to encode the automaton. We use chain-of-thought and few-shot methods for prompt engineering and demonstrate that our method works using these approaches. Additionally, LARL-RM allows for fully closed-loop reinforcement learning without the need for an expert to guide and supervise the learning since LARL-RM can use the LLM directly to generate the required high-level knowledge for the task at hand. We also show the theoretical guarantee of our algorithm to converge to an optimal policy. We demonstrate that LARL-RM speeds up the convergence by 30% by implementing our method in two case studies. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2402.07069v1-abstract-full').style.display = 'none'; document.getElementById('2402.07069v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 10 February, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> February 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.17173">arXiv:2401.17173</a> <span> [<a href="https://arxiv.org/pdf/2401.17173">pdf</a>, <a href="https://arxiv.org/format/2401.17173">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> </div> </div> <p class="title is-5 mathjax"> Zero-Shot Reinforcement Learning via Function Encoders </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ingebrand%2C+T">Tyler Ingebrand</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+A">Amy Zhang</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2401.17173v2-abstract-short" style="display: inline;"> Although reinforcement learning (RL) can solve many challenging sequential decision making problems, achieving zero-shot transfer across related tasks remains a challenge. The difficulty lies in finding a good representation for the current task so that the agent understands how it relates to previously seen tasks. To achieve zero-shot transfer, we introduce the function encoder, a representation… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.17173v2-abstract-full').style.display = 'inline'; document.getElementById('2401.17173v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.17173v2-abstract-full" style="display: none;"> Although reinforcement learning (RL) can solve many challenging sequential decision making problems, achieving zero-shot transfer across related tasks remains a challenge. The difficulty lies in finding a good representation for the current task so that the agent understands how it relates to previously seen tasks. To achieve zero-shot transfer, we introduce the function encoder, a representation learning algorithm which represents a function as a weighted combination of learned, non-linear basis functions. By using a function encoder to represent the reward function or the transition function, the agent has information on how the current task relates to previously seen tasks via a coherent vector representation. Thus, the agent is able to achieve transfer between related tasks at run time with no additional training. We demonstrate state-of-the-art data efficiency, asymptotic performance, and training stability in three RL fields by augmenting basic RL algorithms with a function encoder task representation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.17173v2-abstract-full').style.display = 'none'; document.getElementById('2401.17173v2-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 11 May, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 30 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.13132">arXiv:2312.13132</a> <span> [<a href="https://arxiv.org/pdf/2312.13132">pdf</a>, <a href="https://arxiv.org/format/2312.13132">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Computational Complexity">cs.CC</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</span> </div> </div> <p class="title is-5 mathjax"> On the complexity of sabotage games for network security </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Raju%2C+D">Dhananjay Raju</a>, <a href="/search/cs?searchtype=author&query=Bakirtzis%2C+G">Georgios Bakirtzis</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.13132v1-abstract-short" style="display: inline;"> Securing dynamic networks against adversarial actions is challenging because of the need to anticipate and counter strategic disruptions by adversarial entities within complex network structures. Traditional game-theoretic models, while insightful, often fail to model the unpredictability and constraints of real-world threat assessment scenarios. We refine sabotage games to reflect the realistic l… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.13132v1-abstract-full').style.display = 'inline'; document.getElementById('2312.13132v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.13132v1-abstract-full" style="display: none;"> Securing dynamic networks against adversarial actions is challenging because of the need to anticipate and counter strategic disruptions by adversarial entities within complex network structures. Traditional game-theoretic models, while insightful, often fail to model the unpredictability and constraints of real-world threat assessment scenarios. We refine sabotage games to reflect the realistic limitations of the saboteur and the network operator. By transforming sabotage games into reachability problems, our approach allows applying existing computational solutions to model realistic restrictions on attackers and defenders within the game. Modifying sabotage games into dynamic network security problems successfully captures the nuanced interplay of strategy and uncertainty in dynamic network security. Theoretically, we extend sabotage games to model network security contexts and thoroughly explore if the additional restrictions raise their computational complexity, often the bottleneck of game theory in practical contexts. Practically, this research sets the stage for actionable insights for developing robust defense mechanisms by understanding what risks to mitigate in dynamically changing networks under threat. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.13132v1-abstract-full').style.display = 'none'; document.getElementById('2312.13132v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 20 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2312.01249">arXiv:2312.01249</a> <span> [<a href="https://arxiv.org/pdf/2312.01249">pdf</a>, <a href="https://arxiv.org/format/2312.01249">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Robotics">cs.RO</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> A Multifidelity Sim-to-Real Pipeline for Verifiable and Compositional Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Neary%2C+C">Cyrus Neary</a>, <a href="/search/cs?searchtype=author&query=Ellis%2C+C">Christian Ellis</a>, <a href="/search/cs?searchtype=author&query=Samyal%2C+A+S">Aryaman Singh Samyal</a>, <a href="/search/cs?searchtype=author&query=Lennon%2C+C">Craig Lennon</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2312.01249v1-abstract-short" style="display: inline;"> We propose and demonstrate a compositional framework for training and verifying reinforcement learning (RL) systems within a multifidelity sim-to-real pipeline, in order to deploy reliable and adaptable RL policies on physical hardware. By decomposing complex robotic tasks into component subtasks and defining mathematical interfaces between them, the framework allows for the independent training a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.01249v1-abstract-full').style.display = 'inline'; document.getElementById('2312.01249v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2312.01249v1-abstract-full" style="display: none;"> We propose and demonstrate a compositional framework for training and verifying reinforcement learning (RL) systems within a multifidelity sim-to-real pipeline, in order to deploy reliable and adaptable RL policies on physical hardware. By decomposing complex robotic tasks into component subtasks and defining mathematical interfaces between them, the framework allows for the independent training and testing of the corresponding subtask policies, while simultaneously providing guarantees on the overall behavior that results from their composition. By verifying the performance of these subtask policies using a multifidelity simulation pipeline, the framework not only allows for efficient RL training, but also for a refinement of the subtasks and their interfaces in response to challenges arising from discrepancies between simulation and reality. In an experimental case study we apply the framework to train and deploy a compositional RL system that successfully pilots a Warthog unmanned ground robot. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2312.01249v1-abstract-full').style.display = 'none'; document.getElementById('2312.01249v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 2 December, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> December 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.14200">arXiv:2311.14200</a> <span> [<a href="https://arxiv.org/pdf/2311.14200">pdf</a>, <a href="https://arxiv.org/format/2311.14200">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Social and Information Networks">cs.SI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Systems and Control">eess.SY</span> </div> </div> <p class="title is-5 mathjax"> Prebunking Design as a Defense Mechanism Against Misinformation Propagation on Social Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bayiz%2C+Y+E">Yigit Ege Bayiz</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.14200v1-abstract-short" style="display: inline;"> The growing reliance on social media for news consumption necessitates effective countermeasures to mitigate the rapid spread of misinformation. Prebunking, a proactive method that arms users with accurate information before they come across false content, has garnered support from journalism and psychology experts. We formalize the problem of optimal prebunking as optimizing the timing of deliver… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.14200v1-abstract-full').style.display = 'inline'; document.getElementById('2311.14200v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.14200v1-abstract-full" style="display: none;"> The growing reliance on social media for news consumption necessitates effective countermeasures to mitigate the rapid spread of misinformation. Prebunking, a proactive method that arms users with accurate information before they come across false content, has garnered support from journalism and psychology experts. We formalize the problem of optimal prebunking as optimizing the timing of delivering accurate information, ensuring users encounter it before receiving misinformation while minimizing the disruption to user experience. Utilizing a susceptible-infected epidemiological process to model the propagation of misinformation, we frame optimal prebunking as a policy synthesis problem with safety constraints. We then propose a policy that approximates the optimal solution to a relaxed problem. The experiments show that this policy cuts the user experience cost of repeated information delivery in half, compared to delivering accurate information immediately after identifying a misinformation propagation. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.14200v1-abstract-full').style.display = 'none'; document.getElementById('2311.14200v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 23 November, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">10 pages, 3 figures, Submitted to PERCOM 2024</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.06275">arXiv:2311.06275</a> <span> [<a href="https://arxiv.org/pdf/2311.06275">pdf</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Algorithmic Robustness </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Jensen%2C+D">David Jensen</a>, <a href="/search/cs?searchtype=author&query=LaMacchia%2C+B">Brian LaMacchia</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a>, <a href="/search/cs?searchtype=author&query=Wisniewski%2C+P">Pamela Wisniewski</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.06275v1-abstract-short" style="display: inline;"> Algorithmic robustness refers to the sustained performance of a computational system in the face of change in the nature of the environment in which that system operates or in the task that the system is meant to perform. Below, we motivate the importance of algorithmic robustness, present a conceptual framework, and highlight the relevant areas of research for which algorithmic robustness is rele… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.06275v1-abstract-full').style.display = 'inline'; document.getElementById('2311.06275v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.06275v1-abstract-full" style="display: none;"> Algorithmic robustness refers to the sustained performance of a computational system in the face of change in the nature of the environment in which that system operates or in the task that the system is meant to perform. Below, we motivate the importance of algorithmic robustness, present a conceptual framework, and highlight the relevant areas of research for which algorithmic robustness is relevant. Why robustness? Robustness is an important enabler of other goals that are frequently cited in the context of public policy decisions about computational systems, including trustworthiness, accountability, fairness, and safety. Despite this dependence, it tends to be under-recognized compared to these other concepts. This is unfortunate, because robustness is often more immediately achievable than these other ultimate goals, which can be more subjective and exacting. Thus, we highlight robustness as an important goal for researchers, engineers, regulators, and policymakers when considering the design, implementation, and deployment of computational systems. We urge researchers and practitioners to elevate the attention paid to robustness when designing and evaluating computational systems. For many key systems, the immediate question after any demonstration of high performance should be: "How robust is that performance to realistic changes in the task or environment?" Greater robustness will set the stage for systems that are more trustworthy, accountable, fair, and safe. Toward that end, this document provides a brief roadmap to some of the concepts and existing research around the idea of algorithmic robustness. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.06275v1-abstract-full').style.display = 'none'; document.getElementById('2311.06275v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 17 October, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2311.06255">arXiv:2311.06255</a> <span> [<a href="https://arxiv.org/pdf/2311.06255">pdf</a>, <a href="https://arxiv.org/ps/2311.06255">ps</a>, <a href="https://arxiv.org/format/2311.06255">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Multiagent Systems">cs.MA</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Artificial Intelligence">cs.AI</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> Privacy-Engineered Value Decomposition Networks for Cooperative Multi-Agent Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Gohari%2C+P">Parham Gohari</a>, <a href="/search/cs?searchtype=author&query=Hale%2C+M">Matthew Hale</a>, <a href="/search/cs?searchtype=author&query=Topcu%2C+U">Ufuk Topcu</a> </p> <p class="abstract mathjax"> <span class="has-text-black-bis has-text-weight-semibold">Abstract</span>: <span class="abstract-short has-text-grey-dark mathjax" id="2311.06255v1-abstract-short" style="display: inline;"> In cooperative multi-agent reinforcement learning (Co-MARL), a team of agents must jointly optimize the team's long-term rewards to learn a designated task. Optimizing rewards as a team often requires inter-agent communication and data sharing, leading to potential privacy implications. We assume privacy considerations prohibit the agents from sharing their environment interaction data. Accordingl… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.06255v1-abstract-full').style.display = 'inline'; document.getElementById('2311.06255v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2311.06255v1-abstract-full" style="display: none;"> In cooperative multi-agent reinforcement learning (Co-MARL), a team of agents must jointly optimize the team's long-term rewards to learn a designated task. Optimizing rewards as a team often requires inter-agent communication and data sharing, leading to potential privacy implications. We assume privacy considerations prohibit the agents from sharing their environment interaction data. Accordingly, we propose Privacy-Engineered Value Decomposition Networks (PE-VDN), a Co-MARL algorithm that models multi-agent coordination while provably safeguarding the confidentiality of the agents' environment interaction data. We integrate three privacy-engineering techniques to redesign the data flows of the VDN algorithm, an existing Co-MARL algorithm that consolidates the agents' environment interaction data to train a central controller that models multi-agent coordination, and develop PE-VDN. In the first technique, we design a distributed computation scheme that eliminates Vanilla VDN's dependency on sharing environment interaction data. Then, we utilize a privacy-preserving multi-party computation protocol to guarantee that the data flows of the distributed computation scheme do not pose new privacy risks. Finally, we enforce differential privacy to preempt inference threats against the agents' training data, past environment interactions, when they take actions based on their neural network predictions. We implement PE-VDN in StarCraft Multi-Agent Competition (SMAC) and show that it achieves 80% of Vanilla VDN's win rate while maintaining differential privacy levels that provide meaningful privacy guarantees. The results demonstrate that PE-VDN can safeguard the confidentiality of agents' environment interaction data without sacrificing multi-agent coordination. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2311.06255v1-abstract-full').style.display = 'none'; document.getElementById('2311.06255v1-abstract-short').style.display = 'inline';">△ Less</a> </span> </p> <p class="is-size-7"><span class="has-text-black-bis has-text-weight-semibold">Submitted</span> 12 September, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2023. </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Comments:</span> <span class="has-text-grey-dark mathjax">Paper accepted at 62nd IEEE Conference on Decision and Control</span> </p> </li> </ol> <nav class="pagination is-small is-centered breathe-horizontal" role="navigation" aria-label="pagination"> <a href="" class="pagination-previous is-invisible">Previous </a> <a href="/search/?searchtype=author&query=Topcu%2C+U&start=50" class="pagination-next" >Next </a> <ul class="pagination-list"> <li> <a href="/search/?searchtype=author&query=Topcu%2C+U&start=0" class="pagination-link is-current" aria-label="Goto page 1">1 </a> </li> <li> <a href="/search/?searchtype=author&query=Topcu%2C+U&start=50" class="pagination-link " aria-label="Page 2" aria-current="page">2 </a> </li> <li> <a href="/search/?searchtype=author&query=Topcu%2C+U&start=100" class="pagination-link " aria-label="Page 3" aria-current="page">3 </a> </li> <li> <a href="/search/?searchtype=author&query=Topcu%2C+U&start=150" class="pagination-link " aria-label="Page 4" aria-current="page">4 </a> </li> </ul> </nav> <div class="is-hidden-tablet">  <span class="help" style="display: inline-block;"><a href="https://github.com/arXiv/arxiv-search/releases">Search v0.5.6 released 2020-02-24</a>  </span> </div> </div> </main> <footer> <div class="columns is-desktop" 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