论逻辑可分性在知识编译中的作用

IF 5.1 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Artificial Intelligence Pub Date : 2024-01-12 DOI:10.1016/j.artint.2024.104077
Junming Qiu , Wenqing Li , Liangda Fang , Quanlong Guan , Zhanhao Xiao , Zhao-Rong Lai , Qian Dong
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引用次数: 0

摘要

知识编译是通过将知识库转换为合适的目标语言来解决高复杂度推理任务的另一种解决方案。由 Levesque 提出的逻辑可分性概念,为两种非凡的语言--可分解否定正则表达式和素蕴式--的子句蕴涵可处理性提供了一般性解释。探索逻辑可分性在问题可处理性中扮演什么角色是很有意思的。在本文中,我们将逻辑可分性的概念应用于命题逻辑背景下的一系列推理问题:可满足性检查(CO)、分句蕴涵检查(CE)、模型计数(CT)、模型枚举(ME)和遗忘(FO),以及它们的双重任务,为几个递归过程做出了贡献。我们提供了相应的基于逻辑分离性的属性:CO-逻辑可分性、CE-逻辑可分性、CT-逻辑可分性、ME-逻辑可分性及其对偶。根据这些属性,我们确定了四种新的正则表达式:CO-LSNNF、CE-LSNNF、CT-LSNNF 和 ME-LSNNF 以及它们的对偶语言。我们证明,它们中的每一种都是相应程序正确的必要条件和充分条件。最后,我们将上述正则表达式整合到知识编译图中。
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On the role of logical separability in knowledge compilation

Knowledge compilation is an alternative solution to address demanding reasoning tasks with high complexity via converting knowledge bases into a suitable target language. The notion of logical separability, proposed by Levesque, offers a general explanation for the tractability of clausal entailment for two remarkable languages: decomposable negation normal form and prime implicates. It is interesting to explore what role logical separability plays in problem tractability. In this paper, we apply the notion of logical separability to a number of reasoning problems within the context of propositional logic: satisfiability checking (CO), clausal entailment checking (CE), model counting (CT), model enumeration (ME) and forgetting (FO), as well as their dual tasks, contributing to several recursive procedures. We provide the corresponding logical separability based properties: CO-logical separability, CE-logical separability, CT-logical separability, ME-logical separability and their duals. Based on these properties, we then identify four novel normal forms: CO-LSNNF, CE-LSNNF, CT-LSNNF and ME-LSNNF, as well as their dual languages. We show that each of them is the necessary and sufficient condition under which the corresponding procedure is correct. We finally integrate the above normal forms into the knowledge compilation map.

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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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