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<button class="button is-small 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/2409.13919">arXiv:2409.13919</a> <span> [<a href="https://arxiv.org/pdf/2409.13919">pdf</a>, <a href="https://arxiv.org/format/2409.13919">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"> Measuring Error Alignment for Decision-Making Systems </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Xu%2C+B">Binxia Xu</a>, <a href="/search/cs?searchtype=author&query=Bikakis%2C+A">Antonis Bikakis</a>, <a href="/search/cs?searchtype=author&query=Onah%2C+D">Daniel Onah</a>, <a href="/search/cs?searchtype=author&query=Vlachidis%2C+A">Andreas Vlachidis</a>, <a href="/search/cs?searchtype=author&query=Dickens%2C+L">Luke Dickens</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="2409.13919v1-abstract-short" style="display: inline;"> Given that AI systems are set to play a pivotal role in future decision-making processes, their trustworthiness and reliability are of critical concern. Due to their scale and complexity, modern AI systems resist direct interpretation, and alternative ways are needed to establish trust in those systems, and determine how well they align with human values. We argue that good measures of the informa… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.13919v1-abstract-full').style.display = 'inline'; document.getElementById('2409.13919v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2409.13919v1-abstract-full" style="display: none;"> Given that AI systems are set to play a pivotal role in future decision-making processes, their trustworthiness and reliability are of critical concern. Due to their scale and complexity, modern AI systems resist direct interpretation, and alternative ways are needed to establish trust in those systems, and determine how well they align with human values. We argue that good measures of the information processing similarities between AI and humans, may be able to achieve these same ends. While Representational alignment (RA) approaches measure similarity between the internal states of two systems, the associated data can be expensive and difficult to collect for human systems. In contrast, Behavioural alignment (BA) comparisons are cheaper and easier, but questions remain as to their sensitivity and reliability. We propose two new behavioural alignment metrics misclassification agreement which measures the similarity between the errors of two systems on the same instances, and class-level error similarity which measures the similarity between the error distributions of two systems. We show that our metrics correlate well with RA metrics, and provide complementary information to another BA metric, within a range of domains, and set the scene for a new approach to value alignment. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2409.13919v1-abstract-full').style.display = 'none'; document.getElementById('2409.13919v1-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 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/2401.12088">arXiv:2401.12088</a> <span> [<a href="https://arxiv.org/pdf/2401.12088">pdf</a>, <a href="https://arxiv.org/format/2401.12088">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> </div> </div> <p class="title is-5 mathjax"> Unsupervised Learning of Graph from Recipes </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Diallo%2C+A">Aissatou Diallo</a>, <a href="/search/cs?searchtype=author&query=Bikakis%2C+A">Antonis Bikakis</a>, <a href="/search/cs?searchtype=author&query=Dickens%2C+L">Luke Dickens</a>, <a href="/search/cs?searchtype=author&query=Hunter%2C+A">Anthony Hunter</a>, <a href="/search/cs?searchtype=author&query=Miller%2C+R">Rob Miller</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="2401.12088v1-abstract-short" style="display: inline;"> Cooking recipes are one of the most readily available kinds of procedural text. They consist of natural language instructions that can be challenging to interpret. In this paper, we propose a model to identify relevant information from recipes and generate a graph to represent the sequence of actions in the recipe. In contrast with other approaches, we use an unsupervised approach. We iteratively… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.12088v1-abstract-full').style.display = 'inline'; document.getElementById('2401.12088v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.12088v1-abstract-full" style="display: none;"> Cooking recipes are one of the most readily available kinds of procedural text. They consist of natural language instructions that can be challenging to interpret. In this paper, we propose a model to identify relevant information from recipes and generate a graph to represent the sequence of actions in the recipe. In contrast with other approaches, we use an unsupervised approach. We iteratively learn the graph structure and the parameters of a $\mathsf{GNN}$ encoding the texts (text-to-graph) one sequence at a time while providing the supervision by decoding the graph into text (graph-to-text) and comparing the generated text to the input. We evaluate the approach by comparing the identified entities with annotated datasets, comparing the difference between the input and output texts, and comparing our generated graphs with those generated by state of the art methods. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.12088v1-abstract-full').style.display = 'none'; document.getElementById('2401.12088v1-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> 22 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/2401.06930">arXiv:2401.06930</a> <span> [<a href="https://arxiv.org/pdf/2401.06930">pdf</a>, <a href="https://arxiv.org/format/2401.06930">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> </div> </div> <p class="title is-5 mathjax"> PizzaCommonSense: Learning to Model Commonsense Reasoning about Intermediate Steps in Cooking Recipes </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Diallo%2C+A">Aissatou Diallo</a>, <a href="/search/cs?searchtype=author&query=Bikakis%2C+A">Antonis Bikakis</a>, <a href="/search/cs?searchtype=author&query=Dickens%2C+L">Luke Dickens</a>, <a href="/search/cs?searchtype=author&query=Hunter%2C+A">Anthony Hunter</a>, <a href="/search/cs?searchtype=author&query=Miller%2C+R">Rob Miller</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="2401.06930v2-abstract-short" style="display: inline;"> Understanding procedural texts, such as cooking recipes, is essential for enabling machines to follow instructions and reason about tasks, a key aspect of intelligent reasoning. In cooking, these instructions can be interpreted as a series of modifications to a food preparation. For a model to effectively reason about cooking recipes, it must accurately discern and understand the inputs and output… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.06930v2-abstract-full').style.display = 'inline'; document.getElementById('2401.06930v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2401.06930v2-abstract-full" style="display: none;"> Understanding procedural texts, such as cooking recipes, is essential for enabling machines to follow instructions and reason about tasks, a key aspect of intelligent reasoning. In cooking, these instructions can be interpreted as a series of modifications to a food preparation. For a model to effectively reason about cooking recipes, it must accurately discern and understand the inputs and outputs of intermediate steps within the recipe. We present a new corpus of cooking recipes enriched with descriptions of intermediate steps that describe the input and output for each step. PizzaCommonsense serves as a benchmark for the reasoning capabilities of LLMs because it demands rigorous explicit input-output descriptions to demonstrate the acquisition of implicit commonsense knowledge, which is unlikely to be easily memorized. GPT-4 achieves only 26\% human-evaluated preference for generations, leaving room for future improvements. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2401.06930v2-abstract-full').style.display = 'none'; document.getElementById('2401.06930v2-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 October, 2024; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 12 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">Findings of EMNLP 2024. The data is available at: https://github.com/adiallo07/PizzaCommonsense</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2310.12309">arXiv:2310.12309</a> <span> [<a href="https://arxiv.org/pdf/2310.12309">pdf</a>, <a href="https://arxiv.org/format/2310.12309">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"> A Unifying Framework for Learning Argumentation Semantics </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Mileva%2C+Z">Zlatina Mileva</a>, <a href="/search/cs?searchtype=author&query=Bikakis%2C+A">Antonis Bikakis</a>, <a href="/search/cs?searchtype=author&query=D%27Asaro%2C+F+A">Fabio Aurelio D'Asaro</a>, <a href="/search/cs?searchtype=author&query=Law%2C+M">Mark Law</a>, <a href="/search/cs?searchtype=author&query=Russo%2C+A">Alessandra Russo</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="2310.12309v1-abstract-short" style="display: inline;"> Argumentation is a very active research field of Artificial Intelligence concerned with the representation and evaluation of arguments used in dialogues between humans and/or artificial agents. Acceptability semantics of formal argumentation systems define the criteria for the acceptance or rejection of arguments. Several software systems, known as argumentation solvers, have been developed to com… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.12309v1-abstract-full').style.display = 'inline'; document.getElementById('2310.12309v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2310.12309v1-abstract-full" style="display: none;"> Argumentation is a very active research field of Artificial Intelligence concerned with the representation and evaluation of arguments used in dialogues between humans and/or artificial agents. Acceptability semantics of formal argumentation systems define the criteria for the acceptance or rejection of arguments. Several software systems, known as argumentation solvers, have been developed to compute the accepted/rejected arguments using such criteria. These include systems that learn to identify the accepted arguments using non-interpretable methods. In this paper we present a novel framework, which uses an Inductive Logic Programming approach to learn the acceptability semantics for several abstract and structured argumentation frameworks in an interpretable way. Through an empirical evaluation we show that our framework outperforms existing argumentation solvers, thus opening up new future research directions in the area of formal argumentation and human-machine dialogues. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2310.12309v1-abstract-full').style.display = 'none'; document.getElementById('2310.12309v1-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, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2023. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2306.09042">arXiv:2306.09042</a> <span> [<a href="https://arxiv.org/pdf/2306.09042">pdf</a>, <a href="https://arxiv.org/ps/2306.09042">ps</a>, <a href="https://arxiv.org/format/2306.09042">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"> A Graphical Formalism for Commonsense Reasoning with Recipes </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bikakis%2C+A">Antonis Bikakis</a>, <a href="/search/cs?searchtype=author&query=Diallo%2C+A">Aissatou Diallo</a>, <a href="/search/cs?searchtype=author&query=Dickens%2C+L">Luke Dickens</a>, <a href="/search/cs?searchtype=author&query=Hunter%2C+A">Anthony Hunter</a>, <a href="/search/cs?searchtype=author&query=Miller%2C+R">Rob Miller</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="2306.09042v1-abstract-short" style="display: inline;"> Whilst cooking is a very important human activity, there has been little consideration given to how we can formalize recipes for use in a reasoning framework. We address this need by proposing a graphical formalization that captures the comestibles (ingredients, intermediate food items, and final products), and the actions on comestibles in the form of a labelled bipartite graph. We then propose f… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.09042v1-abstract-full').style.display = 'inline'; document.getElementById('2306.09042v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2306.09042v1-abstract-full" style="display: none;"> Whilst cooking is a very important human activity, there has been little consideration given to how we can formalize recipes for use in a reasoning framework. We address this need by proposing a graphical formalization that captures the comestibles (ingredients, intermediate food items, and final products), and the actions on comestibles in the form of a labelled bipartite graph. We then propose formal definitions for comparing recipes, for composing recipes from subrecipes, and for deconstructing recipes into subrecipes. We also introduce and compare two formal definitions for substitution into recipes which are required when there are missing ingredients, or some actions are not possible, or because there is a need to change the final product somehow. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2306.09042v1-abstract-full').style.display = 'none'; document.getElementById('2306.09042v1-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, 2023; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 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</span> </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2109.08425">arXiv:2109.08425</a> <span> [<a href="https://arxiv.org/pdf/2109.08425">pdf</a>, <a href="https://arxiv.org/ps/2109.08425">ps</a>, <a href="https://arxiv.org/format/2109.08425">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"> Repurposing of Resources: from Everyday Problem Solving through to Crisis Management </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bikakis%2C+A">Antonis Bikakis</a>, <a href="/search/cs?searchtype=author&query=Dickens%2C+L">Luke Dickens</a>, <a href="/search/cs?searchtype=author&query=Hunter%2C+A">Anthony Hunter</a>, <a href="/search/cs?searchtype=author&query=Miller%2C+R">Rob Miller</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="2109.08425v1-abstract-short" style="display: inline;"> The human ability to repurpose objects and processes is universal, but it is not a well-understood aspect of human intelligence. Repurposing arises in everyday situations such as finding substitutes for missing ingredients when cooking, or for unavailable tools when doing DIY. It also arises in critical, unprecedented situations needing crisis management. After natural disasters and during wartime… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.08425v1-abstract-full').style.display = 'inline'; document.getElementById('2109.08425v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2109.08425v1-abstract-full" style="display: none;"> The human ability to repurpose objects and processes is universal, but it is not a well-understood aspect of human intelligence. Repurposing arises in everyday situations such as finding substitutes for missing ingredients when cooking, or for unavailable tools when doing DIY. It also arises in critical, unprecedented situations needing crisis management. After natural disasters and during wartime, people must repurpose the materials and processes available to make shelter, distribute food, etc. Repurposing is equally important in professional life (e.g. clinicians often repurpose medicines off-license) and in addressing societal challenges (e.g. finding new roles for waste products,). Despite the importance of repurposing, the topic has received little academic attention. By considering examples from a variety of domains such as every-day activities, drug repurposing and natural disasters, we identify some principle characteristics of the process and describe some technical challenges that would be involved in modelling and simulating it. We consider cases of both substitution, i.e. finding an alternative for a missing resource, and exploitation, i.e. identifying a new role for an existing resource. We argue that these ideas could be developed into general formal theory of repurposing, and that this could then lead to the development of AI methods based on commonsense reasoning, argumentation, ontological reasoning, and various machine learning methods, to develop tools to support repurposing in practice. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2109.08425v1-abstract-full').style.display = 'none'; document.getElementById('2109.08425v1-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 September, 2021; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> September 2021. </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">16 pages</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.4; I.2.6; I.2.7 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2010.01745">arXiv:2010.01745</a> <span> [<a href="https://arxiv.org/pdf/2010.01745">pdf</a>, <a href="https://arxiv.org/format/2010.01745">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="Machine Learning">cs.LG</span> </div> </div> <p class="title is-5 mathjax"> On the Effects of Knowledge-Augmented Data in Word Embeddings </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Ramirez-Echavarria%2C+D">Diego Ramirez-Echavarria</a>, <a href="/search/cs?searchtype=author&query=Bikakis%2C+A">Antonis Bikakis</a>, <a href="/search/cs?searchtype=author&query=Dickens%2C+L">Luke Dickens</a>, <a href="/search/cs?searchtype=author&query=Miller%2C+R">Rob Miller</a>, <a href="/search/cs?searchtype=author&query=Vlachidis%2C+A">Andreas Vlachidis</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="2010.01745v1-abstract-short" style="display: inline;"> This paper investigates techniques for knowledge injection into word embeddings learned from large corpora of unannotated data. These representations are trained with word cooccurrence statistics and do not commonly exploit syntactic and semantic information from linguistic knowledge bases, which potentially limits their transferability to domains with differing language distributions or usages. W… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.01745v1-abstract-full').style.display = 'inline'; document.getElementById('2010.01745v1-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2010.01745v1-abstract-full" style="display: none;"> This paper investigates techniques for knowledge injection into word embeddings learned from large corpora of unannotated data. These representations are trained with word cooccurrence statistics and do not commonly exploit syntactic and semantic information from linguistic knowledge bases, which potentially limits their transferability to domains with differing language distributions or usages. We propose a novel approach for linguistic knowledge injection through data augmentation to learn word embeddings that enforce semantic relationships from the data, and systematically evaluate the impact it has on the resulting representations. We show our knowledge augmentation approach improves the intrinsic characteristics of the learned embeddings while not significantly altering their results on a downstream text classification task. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2010.01745v1-abstract-full').style.display = 'none'; document.getElementById('2010.01745v1-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 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> October 2020. </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, 5 figures, submitted to ACL 2020</span> </p> <p class="comments is-size-7"> <span class="has-text-black-bis has-text-weight-semibold">ACM Class:</span> I.2.7 </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/2006.11097">arXiv:2006.11097</a> <span> [<a href="https://arxiv.org/pdf/2006.11097">pdf</a>, <a href="https://arxiv.org/format/2006.11097">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="Logic in Computer Science">cs.LO</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"> Contextual and Possibilistic Reasoning for Coalition Formation </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=Bikakis%2C+A">Antonis Bikakis</a>, <a href="/search/cs?searchtype=author&query=Caire%2C+P">Patrice Caire</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="2006.11097v2-abstract-short" style="display: inline;"> In multiagent systems, agents often have to rely on other agents to reach their goals, for example when they lack a needed resource or do not have the capability to perform a required action. Agents therefore need to cooperate. Then, some of the questions raised are: Which agent(s) to cooperate with? What are the potential coalitions in which agents can achieve their goals? As the number of possib… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.11097v2-abstract-full').style.display = 'inline'; document.getElementById('2006.11097v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="2006.11097v2-abstract-full" style="display: none;"> In multiagent systems, agents often have to rely on other agents to reach their goals, for example when they lack a needed resource or do not have the capability to perform a required action. Agents therefore need to cooperate. Then, some of the questions raised are: Which agent(s) to cooperate with? What are the potential coalitions in which agents can achieve their goals? As the number of possibilities is potentially quite large, how to automate the process? And then, how to select the most appropriate coalition, taking into account the uncertainty in the agents' abilities to carry out certain tasks? In this article, we address the question of how to find and evaluate coalitions among agents in multiagent systems using MCS tools, while taking into consideration the uncertainty around the agents' actions. Our methodology is the following: We first compute the solution space for the formation of coalitions using a contextual reasoning approach. Second, we model agents as contexts in Multi-Context Systems (MCS), and dependence relations among agents seeking to achieve their goals, as bridge rules. Third, we systematically compute all potential coalitions using algorithms for MCS equilibria, and given a set of functional and non-functional requirements, we propose ways to select the best solutions. Finally, in order to handle the uncertainty in the agents' actions, we extend our approach with features of possibilistic reasoning. We illustrate our approach with an example from robotics. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('2006.11097v2-abstract-full').style.display = 'none'; document.getElementById('2006.11097v2-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> 6 October, 2020; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 19 June, 2020; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> June 2020. </p> </li> <li class="arxiv-result"> <div class="is-marginless"> <p class="list-title is-inline-block"><a href="https://arxiv.org/abs/1703.06815">arXiv:1703.06815</a> <span> [<a href="https://arxiv.org/pdf/1703.06815">pdf</a>, <a href="https://arxiv.org/ps/1703.06815">ps</a>, <a href="https://arxiv.org/format/1703.06815">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"> Foundations for a Probabilistic Event Calculus </p> <p class="authors"> <span class="search-hit">Authors:</span> <a href="/search/cs?searchtype=author&query=D%27Asaro%2C+F+A">Fabio Aurelio D'Asaro</a>, <a href="/search/cs?searchtype=author&query=Bikakis%2C+A">Antonis Bikakis</a>, <a href="/search/cs?searchtype=author&query=Dickens%2C+L">Luke Dickens</a>, <a href="/search/cs?searchtype=author&query=Miller%2C+R">Rob Miller</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="1703.06815v2-abstract-short" style="display: inline;"> We present PEC, an Event Calculus (EC) style action language for reasoning about probabilistic causal and narrative information. It has an action language style syntax similar to that of the EC variant Modular-E. Its semantics is given in terms of possible worlds which constitute possible evolutions of the domain, and builds on that of EFEC, an epistemic extension of EC. We also describe an ASP im… <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1703.06815v2-abstract-full').style.display = 'inline'; document.getElementById('1703.06815v2-abstract-short').style.display = 'none';">▽ More</a> </span> <span class="abstract-full has-text-grey-dark mathjax" id="1703.06815v2-abstract-full" style="display: none;"> We present PEC, an Event Calculus (EC) style action language for reasoning about probabilistic causal and narrative information. It has an action language style syntax similar to that of the EC variant Modular-E. Its semantics is given in terms of possible worlds which constitute possible evolutions of the domain, and builds on that of EFEC, an epistemic extension of EC. We also describe an ASP implementation of PEC and show the sense in which this is sound and complete. <a class="is-size-7" style="white-space: nowrap;" onclick="document.getElementById('1703.06815v2-abstract-full').style.display = 'none'; document.getElementById('1703.06815v2-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 June, 2017; <span class="has-text-black-bis has-text-weight-semibold">v1</span> submitted 20 March, 2017; <span class="has-text-black-bis has-text-weight-semibold">originally announced</span> March 2017. </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">Technical report</span> </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 9.7-.2 9.7 4.7V400c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V195.6c0-5 5.7-7.8 9.7-4.7 22.4 17.4 52.1 39.5 154.1 113.6 21.1 15.4 56.7 47.8 92.2 47.6 35.7.3 72-32.8 92.3-47.6 102-74.1 131.6-96.3 154-113.7zM256 320c23.2.4 56.6-29.2 73.4-41.4 132.7-96.3 142.8-104.7 173.4-128.7 5.8-4.5 9.2-11.5 9.2-18.9v-19c0-26.5-21.5-48-48-48H48C21.5 64 0 85.5 0 112v19c0 7.4 3.4 14.3 9.2 18.9 30.6 23.9 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