144 research outputs found

    A Brief History of Updates of Answer-Set Programs

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    Funding Information: The authors would like to thank José Alferes, Martin Baláz, Federico Banti, Antonio Brogi, Martin Homola, Luís Moniz Pereira, Halina Przymusinska, Teodor C. Przymusinski, and Theresa Swift, with whom they worked on the topic of this paper over the years, as well as Ricardo Gonçalves and Matthias Knorr for valuable comments on an earlier draft of this paper. The authors would also like to thank the anonymous reviewers for their insightful comments and suggestions, which greatly helped us improve this paper. The authors were partially supported by Fundação para a Ciência e Tecnologia through projects FORGET (PTDC/CCI-INF/32219/2017) and RIVER (PTDC/CCI-COM/30952/2017), and strategic project NOVA LINCS (UIDB/04516/2020). Publisher Copyright: © The Author(s), 2022. Published by Cambridge University Press.Over the last couple of decades, there has been a considerable effort devoted to the problem of updating logic programs under the stable model semantics (a.k.a. answer-set programs) or, in other words, the problem of characterising the result of bringing up-to-date a logic program when the world it describes changes. Whereas the state-of-the-art approaches are guided by the same basic intuitions and aspirations as belief updates in the context of classical logic, they build upon fundamentally different principles and methods, which have prevented a unifying framework that could embrace both belief and rule updates. In this paper, we will overview some of the main approaches and results related to answer-set programming updates, while pointing out some of the main challenges that research in this topic has faced.publishersversionpublishe

    A Semantic Characterization for ASP Base Revision

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    International audienceThe paper deals with base revision for Answer Set Programming (ASP). Base revision in classical logic is done by the removal of formulas. Exploiting the non-monotonicity of ASP allows one to propose other revision strategies, namely addition strategy or removal and/or addition strategy. These strategies allow one to define families of rule-based revision operators. The paper presents a semantic characterization of these families of revision operators in terms of answer sets. This semantic characterization allows for equivalently considering the evolution of syntactic logic programs and the evolution of their semantic content. It then studies the logical properties of the proposed operators and gives complexity results

    Belief Change in Reasoning Agents: Axiomatizations, Semantics and Computations

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    The capability of changing beliefs upon new information in a rational and efficient way is crucial for an intelligent agent. Belief change therefore is one of the central research fields in Artificial Intelligence (AI) for over two decades. In the AI literature, two different kinds of belief change operations have been intensively investigated: belief update, which deal with situations where the new information describes changes of the world; and belief revision, which assumes the world is static. As another important research area in AI, reasoning about actions mainly studies the problem of representing and reasoning about effects of actions. These two research fields are closely related and apply a common underlying principle, that is, an agent should change its beliefs (knowledge) as little as possible whenever an adjustment is necessary. This lays down the possibility of reusing the ideas and results of one field in the other, and vice verse. This thesis aims to develop a general framework and devise computational models that are applicable in reasoning about actions. Firstly, I shall propose a new framework for iterated belief revision by introducing a new postulate to the existing AGM/DP postulates, which provides general criteria for the design of iterated revision operators. Secondly, based on the new framework, a concrete iterated revision operator is devised. The semantic model of the operator gives nice intuitions and helps to show its satisfiability of desirable postulates. I also show that the computational model of the operator is almost optimal in time and space-complexity. In order to deal with the belief change problem in multi-agent systems, I introduce a concept of mutual belief revision which is concerned with information exchange among agents. A concrete mutual revision operator is devised by generalizing the iterated revision operator. Likewise, a semantic model is used to show the intuition and many nice properties of the mutual revision operator, and the complexity of its computational model is formally analyzed. Finally, I present a belief update operator, which takes into account two important problems of reasoning about action, i.e., disjunctive updates and domain constraints. Again, the updated operator is presented with both a semantic model and a computational model

    Computational Complexity of Strong Admissibility for Abstract Dialectical Frameworks

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    Abstract dialectical frameworks (ADFs) have been introduced as a formalism for modeling and evaluating argumentation allowing general logical satisfaction conditions. Different criteria used to settle the acceptance of arguments arecalled semantics. Semantics of ADFs have so far mainly been defined based on the concept of admissibility. Recently, the notion of strong admissibility has been introduced for ADFs. In the current work we study the computational complexityof the following reasoning tasks under strong admissibility semantics. We address 1. the credulous/skeptical decision problem; 2. the verification problem; 3. the strong justification problem; and 4. the problem of finding a smallest witness of strong justification of a queried argument

    Rational Agents: Prioritized Goals, Goal Dynamics, and Agent Programming Languages with Declarative Goals

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    I introduce a specification language for modeling an agent's prioritized goals and their dynamics. I use the situation calculus along with Reiter's solution to the frame problem and predicates for describing agents' knowledge as my base formalism. I further enhance this language by introducing a new sort of infinite paths. Within this language, I discuss how to systematically specify prioritized goals and how to precisely describe the effects of actions on these goals. These actions include adoption and dropping of goals and subgoals. In this framework, an agent's intentions are formally specified as the prioritized intersection of her goals. The ``prioritized'' qualifier above means that the specification must respect the priority ordering of goals when choosing between two incompatible goals. I ensure that the agent's intentions are always consistent with each other and with her knowledge. I investigate two variants with different commitment strategies. Agents specified using the ``optimizing'' agent framework always try to optimize their intentions, while those specified in the ``committed'' agent framework will stick to their intentions even if opportunities to commit to higher priority goals arise when these goals are incompatible with their current intentions. For these, I study properties of prioritized goals and goal change. I also give a definition of subgoals, and prove properties about the goal-subgoal relationship. As an application, I develop a model for a Simple Rational Agent Programming Language (SR-APL) with declarative goals. SR-APL is based on the ``committed agent'' variant of this rich theory, and combines elements from Belief-Desire-Intention (BDI) APLs and the situation calculus based ConGolog APL. Thus SR-APL supports prioritized goals and is grounded on a formal theory of goal change. It ensures that the agent's declarative goals and adopted plans are consistent with each other and with her knowledge. In doing this, I try to bridge the gap between agent theories and practical agent programming languages by providing a model and specification of an idealized BDI agent whose behavior is closer to what a rational agent does. I show that agents programmed in SR-APL satisfy some key rationality requirements
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