不完整论证框架的基础语义学和基于原则的分析

IF 3.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Jean-Guy Mailly
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引用次数: 0

摘要

不完备论证框架(IAFs)以其实际存在性值得怀疑的论点和攻击丰富了经典的抽象论证。通常的推理方法依赖于 "完成 "的概念,即标准论证框架代表的 "可能世界 "与 IAF 中编码的不确定信息相兼容。最近,有人定义了不依赖于完备概念的基于扩展的 IAF 语义,并使用了新版本的无冲突性和防御性,将论证和攻击的(确定或不确定)性质考虑在内。在本文中,我们将对 "基于完成 "和 "直接 "的推理方法提出新的见解。首先,我们将众所周知的基础语义改编成两种不同的版本,使其不依赖于完备性。在确定我们的新语义是多项式可计算的之后,我们对这些语义以及之前在文献中定义的 "直接 "语义(即完备语义、优先语义和稳定语义)进行了基于原理的分析。最后,我们还提供了经典的 "基于完备性 "语义满足原则的新结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Grounded semantics and principle-based analysis for incomplete argumentation frameworks

Incomplete Argumentation Frameworks (IAFs) enrich classical abstract argumentation with arguments and attacks whose actual existence is questionable. The usual reasoning approaches rely on the notion of completion, i.e. standard AFs representing “possible worlds” compatible with the uncertain information encoded in the IAF. Recently, extension-based semantics for IAFs that do not rely on the notion of completion have been defined, using instead new versions of conflict-freeness and defense that take into account the (certain or uncertain) nature of arguments and attacks. In this paper, we give new insights on both the “completion-based” and the “direct” reasoning approaches. First, we adapt the well-known grounded semantics to this framework in two different versions that do not rely on completions. After determining that our new semantics are polynomially computable, we provide a principle-based analysis of these semantics, as well as the “direct” semantics previously defined in the literature, namely the complete, preferred and stable semantics. Finally, we also provide new results regarding the satisfaction of principles by the classical “completion-based” semantics.

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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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