Minimal Coverability Set for Petri Nets: Karp and Miller Algorithm with Pruning

IF 0.4 4区计算机科学 Q4 COMPUTER SCIENCE, SOFTWARE ENGINEERING Fundamenta Informaticae Pub Date : 2011-06-20 DOI:10.3233/FI-2013-781

P. Reynier, Frédéric Servais

引用次数: 35

Abstract

This paper presents the Monotone-Pruning algorithm (MP) for computing the minimal coverability set of Petri nets. The original Karp and Miller algorithm (K&M) unfolds the reachability graph of a Petri net and uses acceleration on branches to ensure termination. The MP algorithm improves the K&M algorithm by adding pruning between branches of the K&M tree. This idea was first introduced in the Minimal Coverability Tree algorithm (MCT), however it was recently shown to be incomplete. The MP algorithm can be viewed as the MCT algorithm with a slightly more aggressive pruning strategy which ensures completeness. Experimental results show that this algorithm is a strong improvement over the K&M algorithm. © 2011 Springer-Verlag.

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Petri网的最小可覆盖性集:带修剪的Karp和Miller算法

本文提出了计算Petri网最小可覆盖性集的单调-剪枝算法。原始的Karp和Miller算法(K&M)展开Petri网的可达图，并在分支上使用加速来确保终止。MP算法通过在K&M树的分支之间添加剪枝来改进K&M算法。这个想法最初是在最小可覆盖性树算法(MCT)中引入的，但最近被证明是不完整的。MP算法可以被看作是MCT算法，具有稍微更激进的修剪策略，以确保完整性。实验结果表明，该算法比K&M算法有较强的改进。©2011施普林格出版社。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Fundamenta Informaticae 工程技术-计算机：软件工程

CiteScore

2.00

自引率

0.00%

发文量

审稿时长

9.8 months

期刊介绍： Fundamenta Informaticae is an international journal publishing original research results in all areas of theoretical computer science. Papers are encouraged contributing: solutions by mathematical methods of problems emerging in computer science solutions of mathematical problems inspired by computer science. Topics of interest include (but are not restricted to): theory of computing, complexity theory, algorithms and data structures, computational aspects of combinatorics and graph theory, programming language theory, theoretical aspects of programming languages, computer-aided verification, computer science logic, database theory, logic programming, automated deduction, formal languages and automata theory, concurrency and distributed computing, cryptography and security, theoretical issues in artificial intelligence, machine learning, pattern recognition, algorithmic game theory, bioinformatics and computational biology, quantum computing, probabilistic methods, algebraic and categorical methods.

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