回答集程序设计中的模态逻辑S5的可满足性

IF 1.4 2区 数学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Mario Alviano, Sotiris Batsakis, George Baryannis
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引用次数: 0

摘要

抽象模态逻辑S5由于其处理嵌套模态算子的简化方法而引起了人们的极大关注,并带来了一些实际应用。评估S5公式可满足性的有效实现通常依赖于Skolemisation将它们转换为命题逻辑公式,本质上是通过为每组解释(可能的世界)引入命题原子的副本。这种方法很简单,但通常会导致难以处理的大型公式,因此需要更简约的构造。在这项工作中,我们建议使用答案集编程来实现这种构造,特别是通过可达性关系来识别在每个世界中相关的命题原子。所提出的编码是为了利用其他性质,如模态算子根的子形式的蕴涵关系。对所提出的编码的经验评估表明,可达性关系非常有效,并导致与最先进的基于SAT的S5求解器相当的性能,而隐含关系可能过于昂贵,难以推理,并且可能导致开销。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Modal Logic S5 Satisfiability in Answer Set Programming
Abstract Modal logic S5 has attracted significant attention and has led to several practical applications, owing to its simplified approach to dealing with nesting modal operators. Efficient implementations for evaluating satisfiability of S5 formulas commonly rely on Skolemisation to convert them into propositional logic formulas, essentially by introducing copies of propositional atoms for each set of interpretations (possible worlds). This approach is simple, but often results into large formulas that are too difficult to process, and therefore more parsimonious constructions are required. In this work, we propose to use Answer Set Programming for implementing such constructions, and in particular for identifying the propositional atoms that are relevant in every world by means of a reachability relation. The proposed encodings are designed to take advantage of other properties such as entailment relations of subformulas rooted by modal operators. An empirical assessment of the proposed encodings shows that the reachability relation is very effective and leads to comparable performance to a state-of-the-art S5 solver based on SAT, while entailment relations are possibly too expensive to reason about and may result in overhead.
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来源期刊
Theory and Practice of Logic Programming
Theory and Practice of Logic Programming 工程技术-计算机:理论方法
CiteScore
4.50
自引率
21.40%
发文量
40
审稿时长
>12 weeks
期刊介绍: Theory and Practice of Logic Programming emphasises both the theory and practice of logic programming. Logic programming applies to all areas of artificial intelligence and computer science and is fundamental to them. Among the topics covered are AI applications that use logic programming, logic programming methodologies, specification, analysis and verification of systems, inductive logic programming, multi-relational data mining, natural language processing, knowledge representation, non-monotonic reasoning, semantic web reasoning, databases, implementations and architectures and constraint logic programming.
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