Embedding justification theory in approximation fixpoint theory

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Artificial Intelligence Pub Date : 2024-03-16 DOI:10.1016/j.artint.2024.104112
Simon Marynissen , Bart Bogaerts , Marc Denecker
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Abstract

Approximation Fixpoint Theory (AFT) and Justification Theory (JT) are two frameworks to unify logical formalisms. AFT studies semantics in terms of fixpoints of lattice operators, and JT in terms of so-called justifications, which are explanations of why certain facts do or do not hold in a model. While the approaches differ, the frameworks were designed with similar goals in mind, namely to study the different semantics that arise in (mainly) non-monotonic logics. The first contribution of our current paper is to provide a formal link between the two frameworks. To be precise, we show that every justification frame induces an approximator and that this mapping from JT to AFT preserves all major semantics. The second contribution exploits this correspondence to extend JT with a novel class of semantics, namely ultimate semantics: we formally show that ultimate semantics can be obtained in JT by a syntactic transformation on the justification frame, essentially performing a sort of resolution on the rules.

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近似定点理论中的嵌入论证理论
近似定点理论(AFT)和理由理论(JT)是统一逻辑形式主义的两个框架。AFT 用网格算子的定点来研究语义,而 JT 则用所谓的理由来研究语义,即解释为什么某些事实在模型中成立或不成立。虽然方法不同,但设计这些框架的目的却相似,即研究(主要是)非单调逻辑中出现的不同语义。我们这篇论文的第一个贡献是提供了这两个框架之间的形式联系。准确地说,我们证明了每一个理由框架都会诱导出一个近似器,而且从 JT 到 AFT 的这种映射保留了所有主要语义。第二个贡献是利用这种对应关系,用一类新颖的语义来扩展 JT,即:我们从形式上证明了终极语义可以在 JT 中通过对理由框架的语法转换来获得,本质上是对规则进行某种解析。
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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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