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Are Good-for-Games Automata Good for Probabilistic Model Checking?

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8370))

Abstract

The potential double exponential blow-up for the generation of deterministic ω-automata for linear temporal logic formulas motivates research on weaker forms of determinism. One of these notions is the good-for-games property that has been introduced by Henzinger and Piterman together with an algorithm for generating good-for-games automata from nondeterministic Büchi automata. The contribution of our paper is twofold. First, we report on an implementation of this algorithms and exhaustive experiments. Second, we show how good-for-games automata can be used for the quantitative analysis of systems modeled by Markov decision processes against ω-regular specifications and evaluate this new method by a series of experiments.

Funded by the DFG through the Graduiertenkolleg 1763 (QuantLA), the CRC 912 HAEC, the cluster of excellence cfAED and the project QuaOS and by the ESF young researcher group IMData 100098198 and the EU-FP-7 grant 295261 (MEALS).

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Klein, J., Müller, D., Baier, C., Klüppelholz, S. (2014). Are Good-for-Games Automata Good for Probabilistic Model Checking?. In: Dediu, AH., Martín-Vide, C., Sierra-Rodríguez, JL., Truthe, B. (eds) Language and Automata Theory and Applications. LATA 2014. Lecture Notes in Computer Science, vol 8370. Springer, Cham. https://doi.org/10.1007/978-3-319-04921-2_37

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  • DOI: https://doi.org/10.1007/978-3-319-04921-2_37

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04920-5

  • Online ISBN: 978-3-319-04921-2

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