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Probabilistic Symbolic Model Checking with PRISM: A Hybrid Approach (2002)
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| Venue: | International Journal on Software Tools for Technology Transfer (STTT |
| Citations: | 201 - 32 self |
BibTeX
@INPROCEEDINGS{Kwiatkowska02probabilisticsymbolic,
author = {Marta Kwiatkowska and Gethin Norman and David Parker},
title = {Probabilistic Symbolic Model Checking with PRISM: A Hybrid Approach},
booktitle = {International Journal on Software Tools for Technology Transfer (STTT},
year = {2002},
pages = {52--66},
publisher = {Springer}
}
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Abstract
In this paper we introduce PRISM, a probabilistic model checker, and describe the ecient symbolic techniques we have developed during its implementation. PRISM is a tool for analysing probabilistic systems. It supports three models: discrete-time Markov chains, continuous-time Markov chains and Markov decision processes. Analysis is performed through model checking speci cations in the probabilistic temporal logics PCTL and CSL. Motivated by the success of model checkers such as SMV, which use BDDs (binary decision diagrams), we have developed an implementation of PCTL and CSL model checking based on MTBDDs (multi-terminal BDDs) and BDDs. Existing work in this direction has been hindered by the generally poor performance of MTBDD-based numerical computation, which is often substantially slower than explicit methods using sparse matrices. We present a novel hybrid technique which combines aspects of symbolic and explicit approaches to overcome these performance problems. For typical examples, we achieve orders of magnitude speed-up compared to MTBDDs and are able to almost match the speed of sparse matrices whilst maintaining considerable space savings.
Keyphrases
hybrid approach probabilistic symbolic model sparse matrix considerable space saving probabilistic temporal logic pctl speci cation novel hybrid technique csl model mtbdd-based numerical computation explicit approach probabilistic system binary decision diagram markov decision model checker ecient symbolic technique discrete-time markov chain probabilistic model checker poor performance typical example continuous-time markov chain multi-terminal bdds performance problem
