格点观点的论证框架

IF 1.2 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Annals of Mathematics and Artificial Intelligence Pub Date : 2023-06-17 DOI:10.1007/s10472-023-09873-y
Mohammed Elaroussi, Lhouari Nourine, Mohammed Said Radjef
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引用次数: 4

摘要

本文的主要目的是为论证框架扩展的研究发展一种格点观点。我们首先通过一个隐含系统的闭集来刻画论证框架的自卫集,该隐含系统可以在多项式时间内从论证框架中计算出来。另一方面,对于自变量集上的任何蕴涵系统\(\ Sigma \),我们将其可容许集为双射的论证框架与\(\西格玛\)的闭集相关联。其次,我们提出了基于蕴涵系统的冲突闭集约简规则,以找出顶点覆盖闭的极小子集,同时保持所有潜在的可容许扩展和优选扩展。这导致了一种多项式延迟和空间算法来枚举论证框架的可接受集合,而不需要偶数循环。最后,基于隐含系统,定义了一种新的论证框架分解,并给出了一种多项式延迟和空间算法来枚举二分论证框架的可容许集。该算法提高了以往算法的指数空间复杂度。
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Lattice point of view for argumentation framework

The main purpose of this article is to develop a lattice point of view for the study of argumentation framework extensions. We first characterize self-defending sets of an argumentation framework by the closed sets of an implicational system that can be computed in polynomial time from the argumentation framework. On the other hand, for any implicational system \(\Sigma \) over the set of arguments, we associate an argumentation framework whose admissible sets are in bijection with closed sets of \(\Sigma \). Second, we propose conflict-closed sets reduction rules, based on implicational system, to find out minimal subsets of vertex cover closed while maintaining all potential admissible extensions as well as preferred extensions. This leads to a polynomial delay and space algorithm to enumerate admissible sets of argumentation frameworks without even cycles. Finally, based on the implicational system, a new decomposition of the argumentation framework is defined and leads to a polynomial delay and space algorithm to enumerate admissible sets for a bipartite argumentation framework. The proposed algorithm improves the exponential space complexity of previous algorithms.

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来源期刊
Annals of Mathematics and Artificial Intelligence
Annals of Mathematics and Artificial Intelligence 工程技术-计算机:人工智能
CiteScore
3.00
自引率
8.30%
发文量
37
审稿时长
>12 weeks
期刊介绍: Annals of Mathematics and Artificial Intelligence presents a range of topics of concern to scholars applying quantitative, combinatorial, logical, algebraic and algorithmic methods to diverse areas of Artificial Intelligence, from decision support, automated deduction, and reasoning, to knowledge-based systems, machine learning, computer vision, robotics and planning. The journal features collections of papers appearing either in volumes (400 pages) or in separate issues (100-300 pages), which focus on one topic and have one or more guest editors. Annals of Mathematics and Artificial Intelligence hopes to influence the spawning of new areas of applied mathematics and strengthen the scientific underpinnings of Artificial Intelligence.
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