条形规划模型的同态与嵌入

IF 1.8 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Arnaud Lequen, Martin C. Cooper, Frédéric Maris
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引用次数: 0

摘要

确定两个strip规划实例是否同构是规划实例之间最简单的比较形式。这也是关于寻找规划实例P $$ P $$和另一个实例P '的子实例之间的同构问题的一个特殊情况。$$ {P}^{\prime } $$。这种映射的一个应用是有效地从包含P ‘的所有解的编译形式生成包含P ’的所有解的编译形式$$ P $$$$ {P}^{\prime } $$。我们还引入了从实例P $$ P $$嵌入到另一个实例P ' $$ {P}^{\prime } $$的概念,这使得我们可以推断P ' $$ {P}^{\prime } $$没有解计划如果P $$ P $$是不可解的。本文研究了这些问题的复杂性。我们证明了第一个问题是gi完备的,因此在理论上可以在拟多项式时间内解决。当我们证明剩下的问题是np完全的时候,我们提出了一个在可能的情况下构建同构的算法。我们报告了对基准问题的大量实验试验,最终证明在预处理中应用约束传播可以大大提高SAT求解器的效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Homomorphisms and Embeddings of STRIPS Planning Models

Homomorphisms and Embeddings of STRIPS Planning Models

Determining whether two STRIPS planning instances are isomorphic is the simplest form of comparison between planning instances. It is also a particular case of the problem concerned with finding an isomorphism between a planning instance P $$ P $$ and a sub-instance of another instance P $$ {P}^{\prime } $$ . One application of such a mapping is to efficiently produce a compiled form containing all solutions to P $$ P $$ from a compiled form containing all solutions to P $$ {P}^{\prime } $$ . We also introduce the notion of embedding from an instance P $$ P $$ to another instance P $$ {P}^{\prime } $$ , which allows us to deduce that P $$ {P}^{\prime } $$ has no solution-plan if P $$ P $$ is unsolvable. In this paper, we study the complexity of these problems. We show that the first is GI-complete and can thus be solved, in theory, in quasi-polynomial time. While we prove the remaining problems to be NP-complete, we propose an algorithm to build an isomorphism when possible. We report extensive experimental trials on benchmark problems that demonstrate conclusively that applying constraint propagation in preprocessing can greatly improve the efficiency of a SAT solver.

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来源期刊
Computational Intelligence
Computational Intelligence 工程技术-计算机:人工智能
CiteScore
6.90
自引率
3.60%
发文量
65
审稿时长
>12 weeks
期刊介绍: This leading international journal promotes and stimulates research in the field of artificial intelligence (AI). Covering a wide range of issues - from the tools and languages of AI to its philosophical implications - Computational Intelligence provides a vigorous forum for the publication of both experimental and theoretical research, as well as surveys and impact studies. The journal is designed to meet the needs of a wide range of AI workers in academic and industrial research.
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