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is-link">Go</button> </div> </div> </form> </div> </div> <ol class="breathe-horizontal" start="1"> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.16312">arXiv:2407.16312</a> <span> [<a href="https://arxiv.org/pdf/2407.16312">pdf</a>, <a href="https://arxiv.org/format/2407.16312">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="Computer Science and Game Theory">cs.GT</span> </div> </div> <p class="title is-5 mathjax"> MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement Learning </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Felten%2C+F">Florian Felten</a>, <a href="/search/cs?searchtype=author&query=Ucak%2C+U">Umut Ucak</a>, <a href="/search/cs?searchtype=author&query=Azmani%2C+H">Hicham Azmani</a>, <a href="/search/cs?searchtype=author&query=Peng%2C+G">Gao Peng</a>, <a href="/search/cs?searchtype=author&query=R%C3%B6pke%2C+W">Willem R枚pke</a>, <a href="/search/cs?searchtype=author&query=Baier%2C+H">Hendrik Baier</a>, <a href="/search/cs?searchtype=author&query=Mannion%2C+P">Patrick Mannion</a>, <a href="/search/cs?searchtype=author&query=Roijers%2C+D+M">Diederik M. Roijers</a>, <a href="/search/cs?searchtype=author&query=Terry%2C+J+K">Jordan K. Terry</a>, <a href="/search/cs?searchtype=author&query=Talbi%2C+E">El-Ghazali Talbi</a>, <a href="/search/cs?searchtype=author&query=Danoy%2C+G">Gr茅goire Danoy</a>, <a href="/search/cs?searchtype=author&query=Now%C3%A9%2C+A">Ann Now茅</a>, <a href="/search/cs?searchtype=author&query=R%C4%83dulescu%2C+R">Roxana R膬dulescu</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="2407.16312v2-abstract-short" style="display: inline;"> Many challenging tasks such as managing traffic systems, electricity grids, or supply chains involve complex decision-making processes that must balance multiple conflicting objectives and coordinate the actions of various independent decision-makers (DMs). One perspective for formalising and addressing such tasks is multi-objective multi-agent reinforcement learning (MOMARL). MOMARL broadens rein… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.16312v2-abstract-full').style.display = 'inline'; document.getElementById('2407.16312v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.16312v2-abstract-full" style="display: none;"> Many challenging tasks such as managing traffic systems, electricity grids, or supply chains involve complex decision-making processes that must balance multiple conflicting objectives and coordinate the actions of various independent decision-makers (DMs). One perspective for formalising and addressing such tasks is multi-objective multi-agent reinforcement learning (MOMARL). MOMARL broadens reinforcement learning (RL) to problems with multiple agents each needing to consider multiple objectives in their learning process. In reinforcement learning research, benchmarks are crucial in facilitating progress, evaluation, and reproducibility. The significance of benchmarks is underscored by the existence of numerous benchmark frameworks developed for various RL paradigms, including single-agent RL (e.g., Gymnasium), multi-agent RL (e.g., PettingZoo), and single-agent multi-objective RL (e.g., MO-Gymnasium). To support the advancement of the MOMARL field, we introduce MOMAland, the first collection of standardised environments for multi-objective multi-agent reinforcement learning. MOMAland addresses the need for comprehensive benchmarking in this emerging field, offering over 10 diverse environments that vary in the number of agents, state representations, reward structures, and utility considerations. To provide strong baselines for future research, MOMAland also includes algorithms capable of learning policies in such settings. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.16312v2-abstract-full').style.display = 'none'; document.getElementById('2407.16312v2-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 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 23 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 2024. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2407.10820">arXiv:2407.10820</a> <span> [<a href="https://arxiv.org/pdf/2407.10820">pdf</a>, <a href="https://arxiv.org/format/2407.10820">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"> Enabling MCTS Explainability for Sequential Planning Through Computation Tree Logic </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=An%2C+Z">Ziyan An</a>, <a href="/search/cs?searchtype=author&query=Baier%2C+H">Hendrik Baier</a>, <a href="/search/cs?searchtype=author&query=Dubey%2C+A">Abhishek Dubey</a>, <a href="/search/cs?searchtype=author&query=Mukhopadhyay%2C+A">Ayan Mukhopadhyay</a>, <a href="/search/cs?searchtype=author&query=Ma%2C+M">Meiyi Ma</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="2407.10820v3-abstract-short" style="display: inline;"> Monte Carlo tree search (MCTS) is one of the most capable online search algorithms for sequential planning tasks, with significant applications in areas such as resource allocation and transit planning. Despite its strong performance in real-world deployment, the inherent complexity of MCTS makes it challenging to understand for users without technical background. This paper considers the use of M… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.10820v3-abstract-full').style.display = 'inline'; document.getElementById('2407.10820v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2407.10820v3-abstract-full" style="display: none;"> Monte Carlo tree search (MCTS) is one of the most capable online search algorithms for sequential planning tasks, with significant applications in areas such as resource allocation and transit planning. Despite its strong performance in real-world deployment, the inherent complexity of MCTS makes it challenging to understand for users without technical background. This paper considers the use of MCTS in transportation routing services, where the algorithm is integrated to develop optimized route plans. These plans are required to meet a range of constraints and requirements simultaneously, further complicating the task of explaining the algorithm's operation in real-world contexts. To address this critical research gap, we introduce a novel computation tree logic-based explainer for MCTS. Our framework begins by taking user-defined requirements and translating them into rigorous logic specifications through the use of language templates. Then, our explainer incorporates a logic verification and quantitative evaluation module that validates the states and actions traversed by the MCTS algorithm. The outcomes of this analysis are then rendered into human-readable descriptive text using a second set of language templates. The user satisfaction of our approach was assessed through a survey with 82 participants. The results indicated that our explanatory approach significantly outperforms other baselines in user preference. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2407.10820v3-abstract-full').style.display = 'none'; document.getElementById('2407.10820v3-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 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 15 July, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> July 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">Accepted by the Proceedings of the 27th European Conference on Artificial Intelligence (ECAI)</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2401.03197">arXiv:2401.03197</a> <span> [<a href="https://arxiv.org/pdf/2401.03197">pdf</a>, <a href="https://arxiv.org/format/2401.03197">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"> Decision Making in Non-Stationary Environments with Policy-Augmented Search </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Pettet%2C+A">Ava Pettet</a>, <a href="/search/cs?searchtype=author&query=Zhang%2C+Y">Yunuo Zhang</a>, <a href="/search/cs?searchtype=author&query=Luo%2C+B">Baiting Luo</a>, <a href="/search/cs?searchtype=author&query=Wray%2C+K">Kyle Wray</a>, <a href="/search/cs?searchtype=author&query=Baier%2C+H">Hendrik Baier</a>, <a href="/search/cs?searchtype=author&query=Laszka%2C+A">Aron Laszka</a>, <a href="/search/cs?searchtype=author&query=Dubey%2C+A">Abhishek Dubey</a>, <a href="/search/cs?searchtype=author&query=Mukhopadhyay%2C+A">Ayan Mukhopadhyay</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="2401.03197v2-abstract-short" style="display: inline;"> Sequential decision-making under uncertainty is present in many important problems. Two popular approaches for tackling such problems are reinforcement learning and online search (e.g., Monte Carlo tree search). While the former learns a policy by interacting with the environment (typically done before execution), the latter uses a generative model of the environment to sample promising action tra… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.03197v2-abstract-full').style.display = 'inline'; document.getElementById('2401.03197v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.03197v2-abstract-full" style="display: none;"> Sequential decision-making under uncertainty is present in many important problems. Two popular approaches for tackling such problems are reinforcement learning and online search (e.g., Monte Carlo tree search). While the former learns a policy by interacting with the environment (typically done before execution), the latter uses a generative model of the environment to sample promising action trajectories at decision time. Decision-making is particularly challenging in non-stationary environments, where the environment in which an agent operates can change over time. Both approaches have shortcomings in such settings -- on the one hand, policies learned before execution become stale when the environment changes and relearning takes both time and computational effort. Online search, on the other hand, can return sub-optimal actions when there are limitations on allowed runtime. In this paper, we introduce \textit{Policy-Augmented Monte Carlo tree search} (PA-MCTS), which combines action-value estimates from an out-of-date policy with an online search using an up-to-date model of the environment. We prove theoretical results showing conditions under which PA-MCTS selects the one-step optimal action and also bound the error accrued while following PA-MCTS as a policy. We compare and contrast our approach with AlphaZero, another hybrid planning approach, and Deep Q Learning on several OpenAI Gym environments. Through extensive experiments, we show that under non-stationary settings with limited time constraints, PA-MCTS outperforms these baselines. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.03197v2-abstract-full').style.display = 'none'; document.getElementById('2401.03197v2-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 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 6 January, 2024; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 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">Extended Abstract accepted for presentation at AAMAS 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/2208.11367">arXiv:2208.11367</a> <span> [<a href="https://arxiv.org/pdf/2208.11367">pdf</a>, <a href="https://arxiv.org/format/2208.11367">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="Cryptography and Security">cs.CR</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"> Combining AI and AM - Improving Approximate Matching through Transformer Networks </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Uhlig%2C+F">Frieder Uhlig</a>, <a href="/search/cs?searchtype=author&query=Struppek%2C+L">Lukas Struppek</a>, <a href="/search/cs?searchtype=author&query=Hintersdorf%2C+D">Dominik Hintersdorf</a>, <a href="/search/cs?searchtype=author&query=G%C3%B6bel%2C+T">Thomas G枚bel</a>, <a href="/search/cs?searchtype=author&query=Baier%2C+H">Harald Baier</a>, <a href="/search/cs?searchtype=author&query=Kersting%2C+K">Kristian Kersting</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="2208.11367v3-abstract-short" style="display: inline;"> Approximate matching (AM) is a concept in digital forensics to determine the similarity between digital artifacts. An important use case of AM is the reliable and efficient detection of case-relevant data structures on a blacklist, if only fragments of the original are available. For instance, if only a cluster of indexed malware is still present during the digital forensic investigation, the AM a… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.11367v3-abstract-full').style.display = 'inline'; document.getElementById('2208.11367v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2208.11367v3-abstract-full" style="display: none;"> Approximate matching (AM) is a concept in digital forensics to determine the similarity between digital artifacts. An important use case of AM is the reliable and efficient detection of case-relevant data structures on a blacklist, if only fragments of the original are available. For instance, if only a cluster of indexed malware is still present during the digital forensic investigation, the AM algorithm shall be able to assign the fragment to the blacklisted malware. However, traditional AM functions like TLSH and ssdeep fail to detect files based on their fragments if the presented piece is relatively small compared to the overall file size. A second well-known issue with traditional AM algorithms is the lack of scaling due to the ever-increasing lookup databases. We propose an improved matching algorithm based on transformer models from the field of natural language processing. We call our approach Deep Learning Approximate Matching (DLAM). As a concept from artificial intelligence (AI), DLAM gets knowledge of characteristic blacklisted patterns during its training phase. Then DLAM is able to detect the patterns in a typically much larger file, that is DLAM focuses on the use case of fragment detection. We reveal that DLAM has three key advantages compared to the prominent conventional approaches TLSH and ssdeep. First, it makes the tedious extraction of known to be bad parts obsolete, which is necessary until now before any search for them with AM algorithms. This allows efficient classification of files on a much larger scale, which is important due to exponentially increasing data to be investigated. Second, depending on the use case, DLAM achieves a similar or even significantly higher accuracy in recovering fragments of blacklisted files. Third, we show that DLAM enables the detection of file correlations in the output of TLSH and ssdeep even for small fragment sizes. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2208.11367v3-abstract-full').style.display = 'none'; document.getElementById('2208.11367v3-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 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 24 August, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2022. </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">Published at DFRWS USA 2023 as a conference paper</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2206.00113">arXiv:2206.00113</a> <span> [<a href="https://arxiv.org/pdf/2206.00113">pdf</a>, <a href="https://arxiv.org/format/2206.00113">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> </div> </div> <p class="title is-5 mathjax"> BRExIt: On Opponent Modelling in Expert Iteration </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Hernandez%2C+D">Daniel Hernandez</a>, <a href="/search/cs?searchtype=author&query=Baier%2C+H">Hendrik Baier</a>, <a href="/search/cs?searchtype=author&query=Kaisers%2C+M">Michael Kaisers</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="2206.00113v2-abstract-short" style="display: inline;"> Finding a best response policy is a central objective in game theory and multi-agent learning, with modern population-based training approaches employing reinforcement learning algorithms as best-response oracles to improve play against candidate opponents (typically previously learnt policies). We propose Best Response Expert Iteration (BRExIt), which accelerates learning in games by incorporatin… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.00113v2-abstract-full').style.display = 'inline'; document.getElementById('2206.00113v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2206.00113v2-abstract-full" style="display: none;"> Finding a best response policy is a central objective in game theory and multi-agent learning, with modern population-based training approaches employing reinforcement learning algorithms as best-response oracles to improve play against candidate opponents (typically previously learnt policies). We propose Best Response Expert Iteration (BRExIt), which accelerates learning in games by incorporating opponent models into the state-of-the-art learning algorithm Expert Iteration (ExIt). BRExIt aims to (1) improve feature shaping in the apprentice, with a policy head predicting opponent policies as an auxiliary task, and (2) bias opponent moves in planning towards the given or learnt opponent model, to generate apprentice targets that better approximate a best response. In an empirical ablation on BRExIt's algorithmic variants against a set of fixed test agents, we provide statistical evidence that BRExIt learns better performing policies than ExIt. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2206.00113v2-abstract-full').style.display = 'none'; document.getElementById('2206.00113v2-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 April, 2023; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 31 May, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2022. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2201.11404">arXiv:2201.11404</a> <span> [<a href="https://arxiv.org/pdf/2201.11404">pdf</a>, <a href="https://arxiv.org/format/2201.11404">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"> Online Planning in POMDPs with Self-Improving Simulators </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=He%2C+J">Jinke He</a>, <a href="/search/cs?searchtype=author&query=Suau%2C+M">Miguel Suau</a>, <a href="/search/cs?searchtype=author&query=Baier%2C+H">Hendrik Baier</a>, <a href="/search/cs?searchtype=author&query=Kaisers%2C+M">Michael Kaisers</a>, <a href="/search/cs?searchtype=author&query=Oliehoek%2C+F+A">Frans A. Oliehoek</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="2201.11404v2-abstract-short" style="display: inline;"> How can we plan efficiently in a large and complex environment when the time budget is limited? Given the original simulator of the environment, which may be computationally very demanding, we propose to learn online an approximate but much faster simulator that improves over time. To plan reliably and efficiently while the approximate simulator is learning, we develop a method that adaptively dec… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.11404v2-abstract-full').style.display = 'inline'; document.getElementById('2201.11404v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2201.11404v2-abstract-full" style="display: none;"> How can we plan efficiently in a large and complex environment when the time budget is limited? Given the original simulator of the environment, which may be computationally very demanding, we propose to learn online an approximate but much faster simulator that improves over time. To plan reliably and efficiently while the approximate simulator is learning, we develop a method that adaptively decides which simulator to use for every simulation, based on a statistic that measures the accuracy of the approximate simulator. This allows us to use the approximate simulator to replace the original simulator for faster simulations when it is accurate enough under the current context, thus trading off simulation speed and accuracy. Experimental results in two large domains show that when integrated with POMCP, our approach allows to plan with improving efficiency over time. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2201.11404v2-abstract-full').style.display = 'none'; document.getElementById('2201.11404v2-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 December, 2022; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 27 January, 2022; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> January 2022. </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">presented at IJCAI 2022</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1808.01262">arXiv:1808.01262</a> <span> [<a href="https://arxiv.org/pdf/1808.01262">pdf</a>, <a href="https://arxiv.org/ps/1808.01262">ps</a>, <a href="https://arxiv.org/format/1808.01262">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"> The Text-Based Adventure AI Competition </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Atkinson%2C+T">Timothy Atkinson</a>, <a href="/search/cs?searchtype=author&query=Baier%2C+H">Hendrik Baier</a>, <a href="/search/cs?searchtype=author&query=Copplestone%2C+T">Tara Copplestone</a>, <a href="/search/cs?searchtype=author&query=Devlin%2C+S">Sam Devlin</a>, <a href="/search/cs?searchtype=author&query=Swan%2C+J">Jerry Swan</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="1808.01262v4-abstract-short" style="display: inline;"> In 2016, 2017, and 2018 at the IEEE Conference on Computational Intelligence in Games, the authors of this paper ran a competition for agents that can play classic text-based adventure games. This competition fills a gap in existing game AI competitions that have typically focussed on traditional card/board games or modern video games with graphical interfaces. By providing a platform for evaluati… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1808.01262v4-abstract-full').style.display = 'inline'; document.getElementById('1808.01262v4-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1808.01262v4-abstract-full" style="display: none;"> In 2016, 2017, and 2018 at the IEEE Conference on Computational Intelligence in Games, the authors of this paper ran a competition for agents that can play classic text-based adventure games. This competition fills a gap in existing game AI competitions that have typically focussed on traditional card/board games or modern video games with graphical interfaces. By providing a platform for evaluating agents in text-based adventures, the competition provides a novel benchmark for game AI with unique challenges for natural language understanding and generation. This paper summarises the three competitions ran in 2016, 2017, and 2018 (including details of open source implementations of both the competition framework and our competitors) and presents the results of an improved evaluation of these competitors across 20 games. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1808.01262v4-abstract-full').style.display = 'none'; document.getElementById('1808.01262v4-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> 24 January, 2019; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 3 August, 2018; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> August 2018. </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">updated to journal version</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">MSC Class:</span> 68T50 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/0911.2174">arXiv:0911.2174</a> <span> [<a href="https://arxiv.org/pdf/0911.2174">pdf</a>, <a href="https://arxiv.org/format/0911.2174">other</a>] </span> </p> <div class="tags is-inline-block"> <span class="tag is-small is-link tooltip is-tooltip-top" data-tooltip="High Energy Physics - Lattice">hep-lat</span> <span class="tag is-small is-grey tooltip is-tooltip-top" data-tooltip="Hardware Architecture">cs.AR</span> </div> </div> <p class="title is-5 mathjax"> QPACE -- a QCD parallel computer based on Cell processors </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Baier%2C+H">H. Baier</a>, <a href="/search/cs?searchtype=author&query=Boettiger%2C+H">H. Boettiger</a>, <a href="/search/cs?searchtype=author&query=Drochner%2C+M">M. Drochner</a>, <a href="/search/cs?searchtype=author&query=Eicker%2C+N">N. Eicker</a>, <a href="/search/cs?searchtype=author&query=Fischer%2C+U">U. Fischer</a>, <a href="/search/cs?searchtype=author&query=Fodor%2C+Z">Z. Fodor</a>, <a href="/search/cs?searchtype=author&query=Frommer%2C+A">A. Frommer</a>, <a href="/search/cs?searchtype=author&query=Gomez%2C+C">C. Gomez</a>, <a href="/search/cs?searchtype=author&query=Goldrian%2C+G">G. Goldrian</a>, <a href="/search/cs?searchtype=author&query=Heybrock%2C+S">S. Heybrock</a>, <a href="/search/cs?searchtype=author&query=Hierl%2C+D">D. Hierl</a>, <a href="/search/cs?searchtype=author&query=H%C3%BCsken%2C+M">M. H眉sken</a>, <a href="/search/cs?searchtype=author&query=Huth%2C+T">T. Huth</a>, <a href="/search/cs?searchtype=author&query=Krill%2C+B">B. Krill</a>, <a href="/search/cs?searchtype=author&query=Lauritsen%2C+J">J. Lauritsen</a>, <a href="/search/cs?searchtype=author&query=Lippert%2C+T">T. Lippert</a>, <a href="/search/cs?searchtype=author&query=Maurer%2C+T">T. Maurer</a>, <a href="/search/cs?searchtype=author&query=Mendl%2C+B">B. Mendl</a>, <a href="/search/cs?searchtype=author&query=Meyer%2C+N">N. Meyer</a>, <a href="/search/cs?searchtype=author&query=Nobile%2C+A">A. Nobile</a>, <a href="/search/cs?searchtype=author&query=Ouda%2C+I">I. Ouda</a>, <a href="/search/cs?searchtype=author&query=Pivanti%2C+M">M. Pivanti</a>, <a href="/search/cs?searchtype=author&query=Pleiter%2C+D">D. Pleiter</a>, <a href="/search/cs?searchtype=author&query=Ries%2C+M">M. Ries</a>, <a href="/search/cs?searchtype=author&query=Sch%C3%A4fer%2C+A">A. Sch盲fer</a> , et al. (10 additional authors not shown) </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="0911.2174v3-abstract-short" style="display: inline;"> QPACE is a novel parallel computer which has been developed to be primarily used for lattice QCD simulations. The compute power is provided by the IBM PowerXCell 8i processor, an enhanced version of the Cell processor that is used in the Playstation 3. The QPACE nodes are interconnected by a custom, application optimized 3-dimensional torus network implemented on an FPGA. To achieve the very hig… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0911.2174v3-abstract-full').style.display = 'inline'; document.getElementById('0911.2174v3-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="0911.2174v3-abstract-full" style="display: none;"> QPACE is a novel parallel computer which has been developed to be primarily used for lattice QCD simulations. The compute power is provided by the IBM PowerXCell 8i processor, an enhanced version of the Cell processor that is used in the Playstation 3. The QPACE nodes are interconnected by a custom, application optimized 3-dimensional torus network implemented on an FPGA. To achieve the very high packaging density of 26 TFlops per rack a new water cooling concept has been developed and successfully realized. In this paper we give an overview of the architecture and highlight some important technical details of the system. Furthermore, we provide initial performance results and report on the installation of 8 QPACE racks providing an aggregate peak performance of 200 TFlops. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('0911.2174v3-abstract-full').style.display = 'none'; document.getElementById('0911.2174v3-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 December, 2009; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 11 November, 2009; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> November 2009. </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. Poster by T. Maurer and plenary talk by D. Pleiter presented at the "XXVII International Symposium on Lattice Field Theory", July 26-31 2009, Peking University, Beijing, China. Information on recent Green500 ranking added and list of authors extended</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">Journal ref:</span> PoS LAT2009:001,2009 </p> </li> </ol> <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" role="navigation" aria-label="Secondary">  <div class="column"> <div class="columns"> <div class="column"> <ul class="nav-spaced"> <li><a href="https://info.arxiv.org/about">About</a></li> <li><a href="https://info.arxiv.org/help">Help</a></li> </ul> </div> <div class="column"> <ul class="nav-spaced"> <li> <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 512 512" class="icon filter-black" role="presentation"><title>contact arXiv</title><desc>Click here to contact arXiv</desc><path d="M502.3 190.8c3.9-3.1 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