A comparison of confluence and ample sets in probabilistic and non-probabilistic branching time.

Theoretical Computer Science(2014)

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摘要
Confluence reduction and partial order reduction by means of ample sets are two different techniques for state space reduction in both traditional and probabilistic model checking. This paper provides an extensive comparison between these two methods, and answers the question how they relate in terms of reduction power when preserving branching time properties. We prove that, while both preserve the same properties, confluence reduction is strictly more powerful than partial order reduction: every reduction that can be obtained with partial order reduction can also be obtained with confluence reduction, but the converse is not true.
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关键词
Confluence reduction,Partial order reduction,Ample sets,Probabilistic branching time,Markov decision processes
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