4 Search Results for "van Breugel, Franck"

Document
DOI: 10.4230/LIPIcs.CONCUR.2019.9
Computing Probabilistic Bisimilarity Distances for Probabilistic Automata

Authors: Giorgio Bacci, Giovanni Bacci, Kim G. Larsen, Radu Mardare, Qiyi Tang, and Franck van Breugel

Published in: LIPIcs, Volume 140, 30th International Conference on Concurrency Theory (CONCUR 2019)

Abstract
The probabilistic bisimilarity distance of Deng et al. has been proposed as a robust quantitative generalization of Segala and Lynch’s probabilistic bisimilarity for probabilistic automata. In this paper, we present a novel characterization of the bisimilarity distance as the solution of a simple stochastic game. The characterization gives us an algorithm to compute the distances by applying Condon’s simple policy iteration on these games. The correctness of Condon’s approach, however, relies on the assumption that the games are stopping. Our games may be non-stopping in general, yet we are able to prove termination for this extended class of games. Already other algorithms have been proposed in the literature to compute these distances, with complexity in UP cap coUP and PPAD. Despite the theoretical relevance, these algorithms are inefficient in practice. To the best of our knowledge, our algorithm is the first practical solution. In the proofs of all the above-mentioned results, an alternative presentation of the Hausdorff distance due to Mémoli plays a central rôle.
Cite as

Giorgio Bacci, Giovanni Bacci, Kim G. Larsen, Radu Mardare, Qiyi Tang, and Franck van Breugel. Computing Probabilistic Bisimilarity Distances for Probabilistic Automata. In 30th International Conference on Concurrency Theory (CONCUR 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 140, pp. 9:1-9:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)

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@InProceedings{bacci_et_al:LIPIcs.CONCUR.2019.9,
  author =	{Bacci, Giorgio and Bacci, Giovanni and Larsen, Kim G. and Mardare, Radu and Tang, Qiyi and van Breugel, Franck},
  title =	{{Computing Probabilistic Bisimilarity Distances for Probabilistic Automata}},
  booktitle =	{30th International Conference on Concurrency Theory (CONCUR 2019)},
  pages =	{9:1--9:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-121-4},
  ISSN =	{1868-8969},
  year =	{2019},
  volume =	{140},
  editor =	{Fokkink, Wan and van Glabbeek, Rob},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CONCUR.2019.9},
  URN =		{urn:nbn:de:0030-drops-109119},
  doi =		{10.4230/LIPIcs.CONCUR.2019.9},
  annote =	{Keywords: Probabilistic automata, Behavioural metrics, Simple stochastic games, Simple policy iteration algorithm}
}
Document
DOI: 10.4230/LIPIcs.CONCUR.2018.9
Deciding Probabilistic Bisimilarity Distance One for Probabilistic Automata

Authors: Qiyi Tang and Franck van Breugel

Published in: LIPIcs, Volume 118, 29th International Conference on Concurrency Theory (CONCUR 2018)

Abstract
Probabilistic bisimilarity, due to Segala and Lynch, is an equivalence relation that captures which states of a probabilistic automaton behave exactly the same. Deng, Chothia, Palamidessi and Pang proposed a robust quantitative generalization of probabilistic bisimilarity. Their probabilistic bisimilarity distances of states of a probabilistic automaton capture the similarity of their behaviour. The smaller the distance, the more alike the states behave. In particular, states are probabilistic bisimilar if and only if their distance is zero. Although the complexity of computing probabilistic bisimilarity distances for probabilistic automata has already been studied and shown to be in NP cap coNP and PPAD, we are not aware of any practical algorithm to compute those distances. In this paper we provide several key results towards algorithms to compute probabilistic bisimilarity distances for probabilistic automata. In particular, we present a polynomial time algorithm that decides distance one. Furthermore, we give an alternative characterization of the probabilistic bisimilarity distances as a basis for a policy iteration algorithm.
Cite as

Qiyi Tang and Franck van Breugel. Deciding Probabilistic Bisimilarity Distance One for Probabilistic Automata. In 29th International Conference on Concurrency Theory (CONCUR 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 118, pp. 9:1-9:17, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@InProceedings{tang_et_al:LIPIcs.CONCUR.2018.9,
  author =	{Tang, Qiyi and van Breugel, Franck},
  title =	{{Deciding Probabilistic Bisimilarity Distance One for Probabilistic Automata}},
  booktitle =	{29th International Conference on Concurrency Theory (CONCUR 2018)},
  pages =	{9:1--9:17},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-087-3},
  ISSN =	{1868-8969},
  year =	{2018},
  volume =	{118},
  editor =	{Schewe, Sven and Zhang, Lijun},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CONCUR.2018.9},
  URN =		{urn:nbn:de:0030-drops-95472},
  doi =		{10.4230/LIPIcs.CONCUR.2018.9},
  annote =	{Keywords: probabilistic automaton, probabilistic bisimilarity, distance}
}
Document
DOI: 10.4230/LIPIcs.CONCUR.2017.27
Algorithms to Compute Probabilistic Bisimilarity Distances for Labelled Markov Chains

Authors: Qiyi Tang and Franck van Breugel

Published in: LIPIcs, Volume 85, 28th International Conference on Concurrency Theory (CONCUR 2017)

Abstract
In the late nineties, Desharnais, Gupta, Jagadeesan and Panangaden presented probabilistic bisimilarity distances on the states of a labelled Markov chain. This provided a quantitative generalisation of probabilistic bisimilarity introduced by Larsen and Skou a decade earlier. In the last decade, several algorithms to approximate and compute these probabilistic bisimilarity distances have been put forward. In this paper, we correct, improve and generalise some of these algorithms. Furthermore, we compare their performance experimentally.
Cite as

Qiyi Tang and Franck van Breugel. Algorithms to Compute Probabilistic Bisimilarity Distances for Labelled Markov Chains. In 28th International Conference on Concurrency Theory (CONCUR 2017). Leibniz International Proceedings in Informatics (LIPIcs), Volume 85, pp. 27:1-27:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2017)

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@InProceedings{tang_et_al:LIPIcs.CONCUR.2017.27,
  author =	{Tang, Qiyi and van Breugel, Franck},
  title =	{{Algorithms to Compute Probabilistic Bisimilarity Distances for Labelled Markov Chains}},
  booktitle =	{28th International Conference on Concurrency Theory (CONCUR 2017)},
  pages =	{27:1--27:16},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-048-4},
  ISSN =	{1868-8969},
  year =	{2017},
  volume =	{85},
  editor =	{Meyer, Roland and Nestmann, Uwe},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CONCUR.2017.27},
  URN =		{urn:nbn:de:0030-drops-77983},
  doi =		{10.4230/LIPIcs.CONCUR.2017.27},
  annote =	{Keywords: labelled Markov chain, probabilistic bisimilarity, pseudometric, policy iteration}
}
Document
DOI: 10.4230/LIPIcs.CONCUR.2016.22
Computing Probabilistic Bisimilarity Distances via Policy Iteration

Authors: Qiyi Tang and Franck van Breugel

Published in: LIPIcs, Volume 59, 27th International Conference on Concurrency Theory (CONCUR 2016)

Abstract
A transformation mapping a labelled Markov chain to a simple stochastic game is presented. In the resulting simple stochastic game, each vertex corresponds to a pair of states of the labelled Markov chain. The value of a vertex of the simple stochastic game is shown to be equal to the probabilistic bisimilarity distance, a notion due to Desharnais, Gupta, Jagadeesan and Panangaden, of the corresponding pair of states of the labelled Markov chain. Bacci, Bacci, Larsen and Mardare introduced an algorithm to compute the probabilistic bisimilarity distances for a labelled Markov chain. A modification of a basic version of their algorithm for a labelled Markov chain is shown to be the policy iteration algorithm applied to the corresponding simple stochastic game. Furthermore, it is shown that this algorithm takes exponential time in the worst case.
Cite as

Qiyi Tang and Franck van Breugel. Computing Probabilistic Bisimilarity Distances via Policy Iteration. In 27th International Conference on Concurrency Theory (CONCUR 2016). Leibniz International Proceedings in Informatics (LIPIcs), Volume 59, pp. 22:1-22:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)

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@InProceedings{tang_et_al:LIPIcs.CONCUR.2016.22,
  author =	{Tang, Qiyi and van Breugel, Franck},
  title =	{{Computing Probabilistic Bisimilarity Distances via Policy Iteration}},
  booktitle =	{27th International Conference on Concurrency Theory (CONCUR 2016)},
  pages =	{22:1--22:15},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-017-0},
  ISSN =	{1868-8969},
  year =	{2016},
  volume =	{59},
  editor =	{Desharnais, Jos\'{e}e and Jagadeesan, Radha},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/LIPIcs.CONCUR.2016.22},
  URN =		{urn:nbn:de:0030-drops-61837},
  doi =		{10.4230/LIPIcs.CONCUR.2016.22},
  annote =	{Keywords: labelled Markov chain, simple stochastic game, probabilistic bisimilarity, pseudometric, value function, policy iteration}
}
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