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        <identifier>oai:drops-oai.dagstuhl.de:1745</identifier>
        <datestamp>2024-03-06T10:33:04Z</datestamp>
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          <dc:title>Algorithms for Game Metrics</dc:title>
          <dc:creator>Chatterjee, Krishnendu</dc:creator>
          <dc:creator>de Alfaro, Luca</dc:creator>
          <dc:creator>Majumdar, Rupak</dc:creator>
          <dc:creator>Raman, Vishwanath</dc:creator>
          <dc:subject>Algorithms</dc:subject>
          <dc:subject>Metrics</dc:subject>
          <dc:subject>Kernel</dc:subject>
          <dc:subject>Simulation</dc:subject>
          <dc:subject>Bisimulation</dc:subject>
          <dc:subject>Linear Programming</dc:subject>
          <dc:subject>Theory of Reals</dc:subject>
          <dc:description>Simulation and bisimulation metrics for stochastic systems provide a&#13;
quantitative generalization of the classical simulation and&#13;
bisimulation relations. &#13;
These metrics capture the similarity of states with respect to&#13;
quantitative specifications written in the quantitative $\mu$-calculus&#13;
and related probabilistic logics.&#13;
&#13;
We present algorithms for computing the metrics on Markov&#13;
decision processes (MDPs), turn-based stochastic games, and concurrent&#13;
games.  &#13;
For turn-based games and MDPs, we provide a polynomial-time algorithm&#13;
based on linear programming &#13;
for the computation of the one-step metric distance between states. &#13;
The algorithm improves on the&#13;
previously known exponential-time algorithm based on a reduction to the theory of&#13;
reals.&#13;
We then present PSPACE algorithms for both the decision problem and the&#13;
problem of approximating the metric distance between two states,&#13;
matching the best known bound for Markov chains. &#13;
For the bisimulation kernel of the metric, which corresponds to probabilistic&#13;
bisimulation, our algorithm works in time $\calo(n^4)$ for both &#13;
turn-based games and MDPs; improving the previously best known &#13;
$\calo(n^9\cdot\log(n))$ time algorithm for MDPs. &#13;
&#13;
For a concurrent game $G$, we show that computing the exact distance&#13;
between states is at least as hard as computing the value of&#13;
concurrent reachability games and&#13;
the square-root-sum problem in computational geometry.&#13;
We show that checking whether the metric distance is bounded by a &#13;
rational $r$, can be accomplished via a reduction to the theory of &#13;
real closed fields, involving a formula with three quantifier &#13;
alternations, yielding $\calo(|G|^{\calo(|G|^5)})$ time &#13;
complexity, improving the previously known reduction with  &#13;
$\calo(|G|^{\calo(|G|^7)})$ time complexity.  &#13;
These algorithms can be iterated to approximate the metrics using&#13;
binary search.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Krishnendu Chatterjee and Luca de Alfaro and Rupak Majumdar and Vishwanath Raman</dc:contributor>
          <dc:date>2008</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 2, IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (2008)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
          <dc:type>doc-type:ResearchArticle</dc:type>
          <dc:type>publishedVersion</dc:type>
          <dc:format>application/pdf</dc:format>
          <dc:identifier>doi:10.4230/LIPIcs.FSTTCS.2008.1745</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-17455</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.FSTTCS.2008.1745</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by-nc-nd/3.0/legalcode</dc:rights>
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