人工知能学会論文誌
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LMNtal並列モデル検査における状態生成数削減及び高速化
安田 竜吉田 健人上田 和紀
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29 巻 (2014) 1 号 p. 182-187

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SLIM is an LMNtal runtime. LMNtal is a programming and modeling language based on hierarchical graph rewriting. SLIM features automata-based LTL model checking that is one of the methods to solve accepting cycle search problems. Parallel search algorithms OWCTY and MAP used by SLIM generate a large number of states for problems having and accepting cycles. Moreover, they have a problem that performance seriously falls for particular problems. We propose a new algorithm that combines MAP and Nested DFS to remove states for problems including accepting cycles. We experimented the algorithm and confirmed improvements both in performance and scalability.

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© 人工知能学会 2014
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