A dialectical formalisation of preferred subtheories reasoning under resource bounds

IF 3.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Kees van Berkel , Marcello D'Agostino , Sanjay Modgil
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引用次数: 0

Abstract

Dialectical Classical Argumentation (Dialectical Cl-Arg) has been shown to satisfy rationality postulates under resource bounds. In particular, the consistency and non-contamination postulates are satisfied despite dropping the assumption of logical omniscience and the consistency and subset minimality checks on arguments' premises that are deployed by standard approaches to Cl-Arg. This paper studies Dialectical Cl-Arg's formalisation of Preferred Subtheories (PS) non-monotonic reasoning under resource bounds. The contribution of this paper is twofold. First, we establish soundness and completeness for Dialectical Cl-Arg's credulous consequence relation under the preferred semantics and credulous PS consequences. This result paves the way for the use of argument game proof theories and dialogues that establish membership of arguments in admissible (and so preferred) extensions, and hence the credulous PS consequences of a belief base. Second, we refine the non-standard characteristic function for Dialectical Cl-Arg, and use this refined function to show soundness for Dialectical Cl-Arg consequences under the grounded semantics and resource-bounded sceptical PS consequence. We provide a counterexample that shows that completeness does not hold. However, we also show that the grounded consequences defined by Dialectical Cl-Arg strictly subsume the grounded consequences defined by standard Cl-Arg formalisations of PS, so that we recover sceptical PS consequences that one would intuitively expect to hold.
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来源期刊
International Journal of Approximate Reasoning
International Journal of Approximate Reasoning 工程技术-计算机:人工智能
CiteScore
6.90
自引率
12.80%
发文量
170
审稿时长
67 days
期刊介绍: The International Journal of Approximate Reasoning is intended to serve as a forum for the treatment of imprecision and uncertainty in Artificial and Computational Intelligence, covering both the foundations of uncertainty theories, and the design of intelligent systems for scientific and engineering applications. It publishes high-quality research papers describing theoretical developments or innovative applications, as well as review articles on topics of general interest. Relevant topics include, but are not limited to, probabilistic reasoning and Bayesian networks, imprecise probabilities, random sets, belief functions (Dempster-Shafer theory), possibility theory, fuzzy sets, rough sets, decision theory, non-additive measures and integrals, qualitative reasoning about uncertainty, comparative probability orderings, game-theoretic probability, default reasoning, nonstandard logics, argumentation systems, inconsistency tolerant reasoning, elicitation techniques, philosophical foundations and psychological models of uncertain reasoning. Domains of application for uncertain reasoning systems include risk analysis and assessment, information retrieval and database design, information fusion, machine learning, data and web mining, computer vision, image and signal processing, intelligent data analysis, statistics, multi-agent systems, etc.
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