稳定模型的一个简单的证明理论表征:归约到差分逻辑和实验

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Martin Gebser, Enrico Giunchiglia, Marco Maratea, Marco Mochi
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引用次数: 0

摘要

许多研究者对逻辑程序的稳定模型进行了研究,并将其与其他形式联系起来加以表征。正如在以前的论文中已经讨论过的那样,由于各种原因,这种表征是有趣的,包括理论研究和导致计算逻辑程序稳定模型的新算法的可能性。在理论层面,复杂性和表现力的比较带来了根本性的洞见。除此之外,开发的约简的实际实现允许使用其他逻辑形式化的现有求解器来计算稳定模型。在本文中,我们首先提供了稳定模型的一个简单表征,它可以被看作是标准模型论定义的证明论对应。我们将进一步展示如何在差异逻辑中自然地对其进行编码。与现有的对经典逻辑的简化相比,这种编码不需要布尔变量。然后,我们将我们的新翻译实现到可满足模理论(SMT)公式。最后,我们将我们的方法(使用SMT求解器工具)与基于翻译的ASP求解器lp2diff进行了比较,并将其与2017年答案集编程竞赛的“基本决策”赛道上的域进行了比较。结果表明,我们的方法可以与lp2diff竞争,并且通常优于lp2diff,并且在非紧域上也可以比clingo更快。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A simple proof-theoretic characterization of stable models: Reduction to difference logic and experiments
Stable models of logic programs have been studied and characterized in relation with other formalisms by many researchers. As already argued in previous papers, such characterizations are interesting for diverse reasons, including theoretical investigations and the possibility of leading to new algorithms for computing stable models of logic programs. At the theoretical level, complexity and expressiveness comparisons have brought about fundamental insights. Beyond that, practical implementations of the developed reductions enable the use of existing solvers for other logical formalisms to compute stable models. In this paper, we first provide a simple characterization of stable models that can be viewed as a proof-theoretic counterpart of the standard model-theoretic definition. We further show how it can be naturally encoded in difference logic. Such an encoding, compared to the existing reductions to classical logics, does not require Boolean variables. Then, we implement our novel translation to a Satisfiability Modulo Theories (SMT) formula. We finally compare our approach, employing the SMT solver yices, to the translation-based ASP solver lp2diff and to clingo on domains from the “Basic Decision” track of the 2017 Answer Set Programming competition. The results show that our approach is competitive to and often better than lp2diff, and that it can also be faster than clingo on non-tight domains.
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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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