Non-deterministic approximation fixpoint theory and its application in disjunctive logic programming

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Artificial Intelligence Pub Date : 2024-03-08 DOI:10.1016/j.artint.2024.104110
Jesse Heyninck , Ofer Arieli , Bart Bogaerts
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Abstract

Approximation fixpoint theory (AFT) is an abstract and general algebraic framework for studying the semantics of nonmonotonic logics. It provides a unifying study of the semantics of different formalisms for nonmonotonic reasoning, such as logic programming, default logic and autoepistemic logic. In this paper, we extend AFT to dealing with non-deterministic constructs that allow to handle indefinite information, represented e.g. by disjunctive formulas. This is done by generalizing the main constructions and corresponding results of AFT to non-deterministic operators, whose ranges are sets of elements rather than single elements. The applicability and usefulness of this generalization is illustrated in the context of disjunctive logic programming.

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非确定性近似定点理论及其在断据逻辑编程中的应用
近似定点理论(AFT)是研究非单调逻辑语义的一个抽象而通用的代数框架。它为非单调推理的不同形式主义(如逻辑编程、缺省逻辑和自显逻辑)提供了统一的语义研究。在本文中,我们将 AFT 扩展到处理非确定性构造,这些构造允许处理不确定的信息,例如,用分界公式表示的信息。这是通过将 AFT 的主要构造和相应结果推广到非确定性算子来实现的,非确定性算子的范围是元素集而不是单个元素。我们将以非定式逻辑编程为背景,说明这种概括的适用性和实用性。
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来源期刊
Artificial Intelligence
Artificial Intelligence 工程技术-计算机:人工智能
CiteScore
11.20
自引率
1.40%
发文量
118
审稿时长
8 months
期刊介绍: The Journal of Artificial Intelligence (AIJ) welcomes papers covering a broad spectrum of AI topics, including cognition, automated reasoning, computer vision, machine learning, and more. Papers should demonstrate advancements in AI and propose innovative approaches to AI problems. Additionally, the journal accepts papers describing AI applications, focusing on how new methods enhance performance rather than reiterating conventional approaches. In addition to regular papers, AIJ also accepts Research Notes, Research Field Reviews, Position Papers, Book Reviews, and summary papers on AI challenges and competitions.
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