Artificial Intelligence最新文献

{"title":"Representing states in iterated belief revision","authors":"Paolo Liberatore","doi":"10.1016/j.artint.2024.104200","DOIUrl":"10.1016/j.artint.2024.104200","url":null,"abstract":"<div><p>Iterated belief revision requires information about the current beliefs. This information is represented by mathematical structures called doxastic states. Most literature concentrates on how to revise a doxastic state and neglects that it may exponentially grow. This problem is studied for the most common ways of storing a doxastic state. All four of them are able to store every doxastic state, but some do it in less space than others. In particular, the explicit representation (an enumeration of the current beliefs) is the more wasteful on space. The level representation (a sequence of propositional formulae) and the natural representation (a history of natural revisions) are more succinct than it. The lexicographic representation (a history of lexicographic revision) is even more succinct than them.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"336 ","pages":"Article 104200"},"PeriodicalIF":5.1,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141910631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On measuring inconsistency in graph databases with regular path constraints","authors":"John Grant ,&nbsp;Francesco Parisi","doi":"10.1016/j.artint.2024.104197","DOIUrl":"10.1016/j.artint.2024.104197","url":null,"abstract":"<div><p>Real-world data are often inconsistent. Although a substantial amount of research has been done on measuring inconsistency, this research concentrated on knowledge bases formalized in propositional logic. Recently, inconsistency measures have been introduced for relational databases. However, nowadays, real-world information is always more frequently represented by graph-based structures which offer a more intuitive conceptualization than relational ones. In this paper, we explore inconsistency measures for graph databases with regular path constraints, a class of integrity constraints based on a well-known navigational language for graph data. In this context, we define several inconsistency measures dealing with specific elements contributing to inconsistency in graph databases. We also define some rationality postulates that are desirable properties for an inconsistency measure for graph databases. We analyze the compliance of each measure with each postulate and find various degrees of satisfaction; in fact, one of the measures satisfies all the postulates. Finally, we investigate the data and combined complexity of the calculation of all the measures as well as the complexity of deciding whether a measure is lower than, equal to, or greater than a given threshold. It turns out that for a majority of the measures these problems are tractable, while for the other different levels of intractability are exhibited.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"335 ","pages":"Article 104197"},"PeriodicalIF":5.1,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0004370224001334/pdfft?md5=113adf90619058fb60d34c4ed866c0e0&pid=1-s2.0-S0004370224001334-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141910662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sample-based bounds for coherent risk measures: Applications to policy synthesis and verification","authors":"Prithvi Akella,&nbsp;Anushri Dixit,&nbsp;Mohamadreza Ahmadi,&nbsp;Joel W. Burdick,&nbsp;Aaron D. Ames","doi":"10.1016/j.artint.2024.104195","DOIUrl":"10.1016/j.artint.2024.104195","url":null,"abstract":"<div><p>Autonomous systems are increasingly used in highly variable and uncertain environments giving rise to the pressing need to consider risk in both the synthesis and verification of policies for these systems. This paper first develops a sample-based method to upper bound the risk measure evaluation of a random variable whose distribution is unknown. These bounds permit us to generate high-confidence verification statements for a large class of robotic systems in a sample-efficient manner. Second, we develop a sample-based method to determine solutions to non-convex optimization problems that outperform a large fraction of the decision space of possible solutions. Both sample-based approaches then permit us to rapidly synthesize risk-aware policies that are guaranteed to achieve a minimum level of system performance. To showcase our approach in simulation, we verify a cooperative multi-agent system and develop a risk-aware controller that outperforms the system's baseline controller. Our approach can be extended to account for any <em>g</em>-entropic risk measure.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"336 ","pages":"Article 104195"},"PeriodicalIF":5.1,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142012116","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Manipulation and peer mechanisms: A survey","authors":"Matthew Olckers ,&nbsp;Toby Walsh","doi":"10.1016/j.artint.2024.104196","DOIUrl":"10.1016/j.artint.2024.104196","url":null,"abstract":"<div><p>In peer mechanisms, the competitors for a prize also determine who wins. Each competitor may be asked to rank, grade, or nominate peers for the prize. Since the prize can be valuable, such as financial aid, course grades, or an award at a conference, competitors may be tempted to manipulate the mechanism. We survey approaches to prevent or discourage the manipulation of peer mechanisms. We conclude our survey by identifying several important research challenges.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"336 ","pages":"Article 104196"},"PeriodicalIF":5.1,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0004370224001322/pdfft?md5=42efb319f0313556632e659a0b77231b&pid=1-s2.0-S0004370224001322-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141910637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NovPhy: A Physical Reasoning Benchmark for Open-world AI Systems","authors":"Vimukthini Pinto ,&nbsp;Chathura Gamage ,&nbsp;Cheng Xue ,&nbsp;Peng Zhang ,&nbsp;Ekaterina Nikonova ,&nbsp;Matthew Stephenson ,&nbsp;Jochen Renz","doi":"10.1016/j.artint.2024.104198","DOIUrl":"10.1016/j.artint.2024.104198","url":null,"abstract":"<div><p>Due to the emergence of AI systems that interact with the physical environment, there is an increased interest in incorporating physical reasoning capabilities into those AI systems. But is it enough to only have physical reasoning capabilities to operate in a real physical environment? In the real world, we constantly face novel situations we have not encountered before. As humans, we are competent at successfully adapting to those situations. Similarly, an agent needs to have the ability to function under the impact of novelties in order to properly operate in an open-world physical environment. To facilitate the development of such AI systems, we propose a new benchmark, NovPhy, that requires an agent to reason about physical scenarios in the presence of novelties and take actions accordingly. The benchmark consists of tasks that require agents to detect and adapt to novelties in physical scenarios. To create tasks in the benchmark, we develop eight novelties representing a diverse novelty space and apply them to five commonly encountered scenarios in a physical environment, related to applying forces and motions such as rolling, falling, and sliding of objects. According to our benchmark design, we evaluate two capabilities of an agent: the performance on a novelty when it is applied to different physical scenarios and the performance on a physical scenario when different novelties are applied to it. We conduct a thorough evaluation with human players, learning agents, and heuristic agents. Our evaluation shows that humans' performance is far beyond the agents' performance. Some agents, even with good normal task performance, perform significantly worse when there is a novelty, and the agents that can adapt to novelties typically adapt slower than humans. We promote the development of intelligent agents capable of performing at the human level or above when operating in open-world physical environments. benchmark website: <span><span>https://github.com/phy-q/novphy</span><svg><path></path></svg></span></p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"336 ","pages":"Article 104198"},"PeriodicalIF":5.1,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0004370224001346/pdfft?md5=387702c1b2d7756ba391c869b2457b2c&pid=1-s2.0-S0004370224001346-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142012115","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Truthful aggregation of budget proposals with proportionality guarantees","authors":"Ioannis Caragiannis ,&nbsp;George Christodoulou ,&nbsp;Nicos Protopapas","doi":"10.1016/j.artint.2024.104178","DOIUrl":"10.1016/j.artint.2024.104178","url":null,"abstract":"<div><p>We study a participatory budgeting problem, where a set of strategic agents wish to split a divisible budget among different projects, by aggregating their proposals on a single division. Unfortunately, the straightforward rule that divides the budget proportionally is susceptible to manipulation. Recently, a class of truthful mechanisms has been proposed, namely the moving phantom mechanisms. One such mechanism satisfies the proportionality property, in the sense that in the extreme case where all agents prefer a single project to receive the whole amount, the budget is assigned proportionally.</p><p>While proportionality is a naturally desired property, it is defined over a limited type of preference profiles. To address this, we expand the notion of proportionality, by proposing a quantitative framework that evaluates a budget aggregation mechanism according to its worst-case distance from the proportional allocation. Crucially, this is defined for every preference profile. We study this measure on the class of moving phantom mechanisms, and we provide approximation guarantees. For two projects, we show that the Uniform Phantom mechanism is optimal among all truthful mechanisms. For three projects, we propose a new, proportional mechanism that is virtually optimal among all moving phantom mechanisms. Finally, we provide impossibility results regarding the approximability of moving phantom mechanisms.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"335 ","pages":"Article 104178"},"PeriodicalIF":5.1,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141892011","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An abstract and structured account of dialectical argument strength","authors":"Henry Prakken","doi":"10.1016/j.artint.2024.104193","DOIUrl":"10.1016/j.artint.2024.104193","url":null,"abstract":"<div><p>This paper presents a formal model of dialectical argument strength in terms of the number of ways in which an argument can be successfully attacked in expansions of an abstract argumentation framework. First a model is proposed that is abstract but designed to avoid overly limiting assumptions on instantiations or dialogue contexts. It is then shown that most principles for argument strength proposed in the literature fail to hold for the proposed notions of dialectical strength, which clarifies the rational foundations of these principles and highlights the importance of distinguishing between kinds of argument strength, in particular logical, dialectical and rhetorical argument strength. The abstract model is then instantiated with <em>ASPIC</em>⁺ to test the claim that it does not make overly limiting assumptions on the structure of arguments and the nature of their relations.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"335 ","pages":"Article 104193"},"PeriodicalIF":5.1,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0004370224001292/pdfft?md5=ccf1642809aafb2e2582cad1cfca01a9&pid=1-s2.0-S0004370224001292-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141891926","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-objective meta-learning","authors":"Feiyang Ye ,&nbsp;Baijiong Lin ,&nbsp;Zhixiong Yue ,&nbsp;Yu Zhang ,&nbsp;Ivor W. Tsang","doi":"10.1016/j.artint.2024.104184","DOIUrl":"10.1016/j.artint.2024.104184","url":null,"abstract":"<div><p>Meta-learning has arisen as a powerful tool for many machine learning problems. With multiple factors to be considered when designing learning models for real-world applications, meta-learning with multiple objectives has attracted much attention recently. However, existing works either linearly combine multiple objectives into one objective or adopt evolutionary algorithms to handle it, where the former approach needs to pay high computational cost to tune the combination coefficients while the latter approach is computationally heavy and incapable to be integrated into gradient-based optimization. To alleviate those limitations, in this paper, we aim to propose a generic gradient-based Multi-Objective Meta-Learning (MOML) framework with applications in many machine learning problems. Specifically, the MOML framework formulates the objective function of meta-learning with multiple objectives as a Multi-Objective Bi-Level optimization Problem (MOBLP) where the upper-level subproblem is to solve several possibly conflicting objectives for the meta-learner. Different from those existing works, in this paper, we propose a gradient-based algorithm to solve the MOBLP. Specifically, we devise the first gradient-based optimization algorithm by alternately solving the lower-level and upper-level subproblems via the gradient descent method and the gradient-based multi-objective optimization method, respectively. Theoretically, we prove the convergence property and provide a non-asymptotic analysis of the proposed gradient-based optimization algorithm. Empirically, extensive experiments justify our theoretical results and demonstrate the superiority of the proposed MOML framework for different learning problems, including few-shot learning, domain adaptation, multi-task learning, neural architecture search, and reinforcement learning. The source code of MOML is available at <span><span>https://github.com/Baijiong-Lin/MOML</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"335 ","pages":"Article 104184"},"PeriodicalIF":5.1,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141851427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A crossword solving system based on Monte Carlo tree search","authors":"Jingping Liu ,&nbsp;Lihan Chen ,&nbsp;Sihang Jiang ,&nbsp;Chao Wang ,&nbsp;Sheng Zhang ,&nbsp;Jiaqing Liang ,&nbsp;Yanghua Xiao ,&nbsp;Rui Song","doi":"10.1016/j.artint.2024.104192","DOIUrl":"10.1016/j.artint.2024.104192","url":null,"abstract":"<div><p>Although the development of AI in games is remarkable, intelligent machines still lag behind humans in games that require the ability of language understanding. In this paper, we focus on the crossword puzzle resolution task. Solving crossword puzzles is a challenging task since it requires the ability to answer natural language questions with knowledge and the ability to execute a search over possible answers to find an optimal set of solutions for the grid. Previous solutions are devoted to exploiting heuristic strategies in search to find solutions while having limited ability to explore the search space. We build a comprehensive system for crossword puzzle resolution based on Monte Carlo Tree Search (MCTS). As far as we know, we are the first to model the crossword puzzle resolution problem as a Markov Decision Process and apply the MCTS to solve it. We construct a dataset for crossword puzzle resolution based on daily puzzles from The New York Times with detailed specifications of both the puzzle and clue database selection. Our method achieves state-of-the-art performance on the dataset. The code of the system and experiments in this paper is publicly available: <span><span>https://www.github.com/lhlclhl/CP</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"335 ","pages":"Article 104192"},"PeriodicalIF":5.1,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141842906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ASQ-IT: Interactive explanations for reinforcement-learning agents","authors":"Yotam Amitai ,&nbsp;Ofra Amir ,&nbsp;Guy Avni","doi":"10.1016/j.artint.2024.104182","DOIUrl":"10.1016/j.artint.2024.104182","url":null,"abstract":"<div><p>As reinforcement learning methods increasingly amass accomplishments, the need for comprehending their solutions becomes more crucial. Most explainable reinforcement learning (XRL) methods generate a static explanation depicting their developers' intuition of what should be explained and how. In contrast, literature from the social sciences proposes that meaningful explanations are structured as a dialog between the explainer and the explainee, suggesting a more active role for the user and her communication with the agent. In this paper, we present ASQ-IT – an interactive explanation system that presents video clips of the agent acting in its environment based on queries given by the user that describe temporal properties of behaviors of interest. Our approach is based on formal methods: queries in ASQ-IT's user interface map to a fragment of Linear Temporal Logic over finite traces (LTLf), which we developed, and our algorithm for query processing is based on automata theory. User studies show that end-users can understand and formulate queries in ASQ-IT and that using ASQ-IT assists users in identifying faulty agent behaviors.</p></div>","PeriodicalId":8434,"journal":{"name":"Artificial Intelligence","volume":"335 ","pages":"Article 104182"},"PeriodicalIF":5.1,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141952324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}