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2017 | OriginalPaper | Chapter

Modal Stochastic Games

Abstraction-Refinement of Probabilistic Automata

Authors : Joost-Pieter Katoen, Falak Sher

Published in: Models, Algorithms, Logics and Tools

Publisher: Springer International Publishing

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Abstract

This paper presents an abstraction-refinement framework for Segala’s probabilistic automata (PA), a slight variant of Markov decision processes. We use Condon and Ladner’s two-player probabilistic game automata extended with possible and required transitions—as in Larsen and Thomsen’s modal transition systems—as abstract models. The key idea is to refine player-one and player-two states separately resulting in a nested abstract-refine loop. We show the adequacy of this approach for obtaining tight bounds on extremal reachability probabilities.

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Footnotes
1
As this paper does not cover parallel composition all PGAs are closed. For modeling PGAs in a compositonal manner though, the distinction between internal and other actions is important, see [7].
 
2
For example, let \(x=\max \) in \( \mathrm {Pr}^x(T^{\prime })\) then \(\mathbf {1}=\max \) and \(\mathbf {2}=\min \) (player-one maximizes whereas the player-two minimizes the probability) or vice versa.
 
3
This may converge slower than allowing for coarser splittings (as in [5]), but yields smaller state spaces.
 
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Metadata
Title
Modal Stochastic Games
Authors
Joost-Pieter Katoen
Falak Sher
Copyright Year
2017
DOI
https://doi.org/10.1007/978-3-319-63121-9_21

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