Faster algorithms for quantitative analysis of MCs and MDPs with small treewidth

Asadi A, Chatterjee K, Goharshady AK, Mohammadi K, Pavlogiannis A. 2020. Faster algorithms for quantitative analysis of MCs and MDPs with small treewidth. Automated Technology for Verification and Analysis. ATVA: Automated Technology for Verification and Analysis, LNCS, vol. 12302, 253–270.

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Department
Series Title
LNCS
Abstract
Discrete-time Markov Chains (MCs) and Markov Decision Processes (MDPs) are two standard formalisms in system analysis. Their main associated quantitative objectives are hitting probabilities, discounted sum, and mean payoff. Although there are many techniques for computing these objectives in general MCs/MDPs, they have not been thoroughly studied in terms of parameterized algorithms, particularly when treewidth is used as the parameter. This is in sharp contrast to qualitative objectives for MCs, MDPs and graph games, for which treewidth-based algorithms yield significant complexity improvements. In this work, we show that treewidth can also be used to obtain faster algorithms for the quantitative problems. For an MC with n states and m transitions, we show that each of the classical quantitative objectives can be computed in O((n+m)⋅t2) time, given a tree decomposition of the MC with width t. Our results also imply a bound of O(κ⋅(n+m)⋅t2) for each objective on MDPs, where κ is the number of strategy-iteration refinements required for the given input and objective. Finally, we make an experimental evaluation of our new algorithms on low-treewidth MCs and MDPs obtained from the DaCapo benchmark suite. Our experiments show that on low-treewidth MCs and MDPs, our algorithms outperform existing well-established methods by one or more orders of magnitude.
Publishing Year
Date Published
2020-10-12
Proceedings Title
Automated Technology for Verification and Analysis
Volume
12302
Page
253-270
Conference
ATVA: Automated Technology for Verification and Analysis
Conference Location
Hanoi, Vietnam
Conference Date
2020-10-19 – 2020-10-23
IST-REx-ID

Cite this

Asadi A, Chatterjee K, Goharshady AK, Mohammadi K, Pavlogiannis A. Faster algorithms for quantitative analysis of MCs and MDPs with small treewidth. In: Automated Technology for Verification and Analysis. Vol 12302. Springer Nature; 2020:253-270. doi:10.1007/978-3-030-59152-6_14
Asadi, A., Chatterjee, K., Goharshady, A. K., Mohammadi, K., & Pavlogiannis, A. (2020). Faster algorithms for quantitative analysis of MCs and MDPs with small treewidth. In Automated Technology for Verification and Analysis (Vol. 12302, pp. 253–270). Hanoi, Vietnam: Springer Nature. https://doi.org/10.1007/978-3-030-59152-6_14
Asadi, Ali, Krishnendu Chatterjee, Amir Kafshdar Goharshady, Kiarash Mohammadi, and Andreas Pavlogiannis. “Faster Algorithms for Quantitative Analysis of MCs and MDPs with Small Treewidth.” In Automated Technology for Verification and Analysis, 12302:253–70. Springer Nature, 2020. https://doi.org/10.1007/978-3-030-59152-6_14.
A. Asadi, K. Chatterjee, A. K. Goharshady, K. Mohammadi, and A. Pavlogiannis, “Faster algorithms for quantitative analysis of MCs and MDPs with small treewidth,” in Automated Technology for Verification and Analysis, Hanoi, Vietnam, 2020, vol. 12302, pp. 253–270.
Asadi A, Chatterjee K, Goharshady AK, Mohammadi K, Pavlogiannis A. 2020. Faster algorithms for quantitative analysis of MCs and MDPs with small treewidth. Automated Technology for Verification and Analysis. ATVA: Automated Technology for Verification and Analysis, LNCS, vol. 12302, 253–270.
Asadi, Ali, et al. “Faster Algorithms for Quantitative Analysis of MCs and MDPs with Small Treewidth.” Automated Technology for Verification and Analysis, vol. 12302, Springer Nature, 2020, pp. 253–70, doi:10.1007/978-3-030-59152-6_14.
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