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        <identifier>oai:drops-oai.dagstuhl.de:6168</identifier>
        <datestamp>2024-03-06T10:38:27Z</datestamp>
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          <dc:title>Minimizing Expected Cost Under Hard Boolean Constraints, with Applications to Quantitative Synthesis</dc:title>
          <dc:creator>Almagor, Shaull</dc:creator>
          <dc:creator>Kupferman, Orna</dc:creator>
          <dc:creator>Velner, Yaron</dc:creator>
          <dc:subject>Stochastic and Quantitative Synthesis</dc:subject>
          <dc:subject>Mean Payoff Games</dc:subject>
          <dc:subject>Sensing.</dc:subject>
          <dc:description>In Boolean synthesis, we are given an LTL specification, and the goal is to construct a transducer that realizes it against an adversarial environment. &#13;
Often, a specification contains both Boolean requirements that should be satisfied against an adversarial environment, and multi-valued components that refer to the quality of the satisfaction and whose expected cost we would like to minimize with respect to a probabilistic environment. &#13;
&#13;
In this work we study, for the first time, mean-payoff games in which the system aims at minimizing the expected cost against a probabilistic environment, while surely satisfying an omega-regular condition against an adversarial environment.&#13;
We consider the case the omega-regular condition is given as a parity objective or by an LTL formula.&#13;
We show that in general, optimal strategies need not exist, and moreover, the limit value cannot be approximated by finite-memory strategies. &#13;
We thus focus on computing the limit-value, and give tight complexity bounds for synthesizing epsilon-optimal strategies for both finite-memory and infinite-memory strategies.&#13;
&#13;
We show that our game naturally arises in various contexts of synthesis with Boolean and multi-valued objectives. Beyond direct applications, in synthesis with costs and rewards to certain behaviors, it allows us to compute the minimal sensing cost of omega-regular specifications -- a measure of quality in which we look for a transducer that minimizes the expected number of signals that are read from the input.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Shaull Almagor and Orna Kupferman and Yaron Velner</dc:contributor>
          <dc:date>2016</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 59, 27th International Conference on Concurrency Theory (CONCUR 2016)</dc:relation>
          <dc:type>InProceedings</dc:type>
          <dc:type>Text</dc:type>
          <dc:type>doc-type:ResearchArticle</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.CONCUR.2016.9</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-61689</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CONCUR.2016.9</dc:identifier>
          <dc:language>eng</dc:language>
          <dc:rights>https://creativecommons.org/licenses/by/3.0/legalcode</dc:rights>
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