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Title:
Learning Weighted Assumptions for Compositional Verification of Markov Decision Processes
Author: He, F ; Gao, XW ; Wang, MF ; Wang, BY ; Zhang, LJ
Keyword: Compositional verification ; probabilistic model checking ; algorithmic learning
Source: ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY
Issued Date: 2016
Volume: 25, Issue:3
Indexed Type: SCI
Department: Tsinghua Univ, MoE, KLiss, Beijing, Peoples R China. Tsinghua Univ, TNList, Beijing, Peoples R China. Tsinghua Univ, Sch Software, Beijing, Peoples R China. Acad Sinica, Taipei, Taiwan. Chinese Acad Sci, Inst Software, State Key Lab Comp Sci, Beijing, Peoples R China.
Abstract: Probabilistic models are widely deployed in various systems. To ensure their correctness, verification techniques have been developed to analyze probabilistic systems. We propose the first sound and complete learning-based compositional verification technique for probabilistic safety properties on concurrent systems where each component is an Markov decision process. Different from previous works, weighted assumptions are introduced to attain completeness of our framework. Since weighted assumptions can be implicitly represented by multiterminal binary decision diagrams (MTBDDs),we give an L*-based learning algorithm for MTBDDs to infer weighted assumptions. Experimental results suggest promising outlooks for our compositional technique.
English Abstract: Probabilistic models are widely deployed in various systems. To ensure their correctness, verification techniques have been developed to analyze probabilistic systems. We propose the first sound and complete learning-based compositional verification technique for probabilistic safety properties on concurrent systems where each component is an Markov decision process. Different from previous works, weighted assumptions are introduced to attain completeness of our framework. Since weighted assumptions can be implicitly represented by multiterminal binary decision diagrams (MTBDDs),we give an L*-based learning algorithm for MTBDDs to infer weighted assumptions. Experimental results suggest promising outlooks for our compositional technique.
Language: 英语
WOS ID: WOS:000382754000002
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Content Type: 期刊论文
URI: http://ir.iscas.ac.cn/handle/311060/17312
Appears in Collections:软件所图书馆_期刊论文

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Recommended Citation:
He, F,Gao, XW,Wang, MF,et al. Learning Weighted Assumptions for Compositional Verification of Markov Decision Processes[J]. ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY,2016-01-01,25(3).
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