{"acknowledgement":"This research was supported by the European Union project COMBEST and the European Network of Excellence ArtistDesign.","volume":6174,"publist_id":"2313","intvolume":"      6174","year":"2010","citation":{"mla":"Chatterjee, Krishnendu, et al. <i>Measuring and Synthesizing Systems in Probabilistic Environments</i>. Vol. 6174, Springer, 2010, pp. 380–95, doi:<a href=\"https://doi.org/10.1007/978-3-642-14295-6_34\">10.1007/978-3-642-14295-6_34</a>.","ieee":"K. Chatterjee, T. A. Henzinger, B. Jobstmann, and R. Singh, “Measuring and synthesizing systems in probabilistic environments,” presented at the CAV: Computer Aided Verification, Edinburgh, United Kingdom, 2010, vol. 6174, pp. 380–395.","ama":"Chatterjee K, Henzinger TA, Jobstmann B, Singh R. Measuring and synthesizing systems in probabilistic environments. In: Vol 6174. Springer; 2010:380-395. doi:<a href=\"https://doi.org/10.1007/978-3-642-14295-6_34\">10.1007/978-3-642-14295-6_34</a>","ista":"Chatterjee K, Henzinger TA, Jobstmann B, Singh R. 2010. Measuring and synthesizing systems in probabilistic environments. CAV: Computer Aided Verification, LNCS, vol. 6174, 380–395.","apa":"Chatterjee, K., Henzinger, T. A., Jobstmann, B., &#38; Singh, R. (2010). Measuring and synthesizing systems in probabilistic environments (Vol. 6174, pp. 380–395). Presented at the CAV: Computer Aided Verification, Edinburgh, United Kingdom: Springer. <a href=\"https://doi.org/10.1007/978-3-642-14295-6_34\">https://doi.org/10.1007/978-3-642-14295-6_34</a>","chicago":"Chatterjee, Krishnendu, Thomas A Henzinger, Barbara Jobstmann, and Rohit Singh. “Measuring and Synthesizing Systems in Probabilistic Environments,” 6174:380–95. Springer, 2010. <a href=\"https://doi.org/10.1007/978-3-642-14295-6_34\">https://doi.org/10.1007/978-3-642-14295-6_34</a>.","short":"K. Chatterjee, T.A. Henzinger, B. Jobstmann, R. Singh, in:, Springer, 2010, pp. 380–395."},"conference":{"location":"Edinburgh, United Kingdom","end_date":"2010-07-19","name":"CAV: Computer Aided Verification","start_date":"201-07-15"},"_id":"3864","language":[{"iso":"eng"}],"author":[{"full_name":"Chatterjee, Krishnendu","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","first_name":"Krishnendu","last_name":"Chatterjee","orcid":"0000-0002-4561-241X"},{"orcid":"0000−0002−2985−7724","last_name":"Henzinger","first_name":"Thomas A","full_name":"Henzinger, Thomas A","id":"40876CD8-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Jobstmann","first_name":"Barbara","full_name":"Jobstmann, Barbara"},{"first_name":"Rohit","last_name":"Singh","full_name":"Singh, Rohit"}],"page":"380 - 395","doi":"10.1007/978-3-642-14295-6_34","status":"public","related_material":{"record":[{"relation":"later_version","id":"1856","status":"public"}]},"quality_controlled":"1","title":"Measuring and synthesizing systems in probabilistic environments","alternative_title":["LNCS"],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_created":"2018-12-11T12:05:35Z","oa_version":"Preprint","oa":1,"scopus_import":1,"publisher":"Springer","publication_status":"published","date_published":"2010-07-09T00:00:00Z","abstract":[{"text":"Often one has a preference order among the different systems that satisfy a given specification. Under a probabilistic assumption about the possible inputs, such a preference order is naturally expressed by a weighted automaton, which assigns to each word a value, such that a system is preferred if it generates a higher expected value. We solve the following optimal-synthesis problem: given an omega-regular specification, a Markov chain that describes the distribution of inputs, and a weighted automaton that measures how well a system satisfies the given specification tinder the given input assumption, synthesize a system that optimizes the measured value. For safety specifications and measures that are defined by mean-payoff automata, the optimal-synthesis problem amounts to finding a strategy in a Markov decision process (MDP) that is optimal for a long-run average reward objective, which can be done in polynomial time. For general omega-regular specifications, the solution rests on a new, polynomial-time algorithm for computing optimal strategies in MDPs with mean-payoff parity objectives. We present some experimental results showing optimal systems that were automatically generated in this way.","lang":"eng"}],"main_file_link":[{"open_access":"1","url":"http://arxiv.org/abs/1004.0739"}],"department":[{"_id":"KrCh"},{"_id":"ToHe"}],"day":"09","type":"conference","date_updated":"2023-02-23T10:17:28Z","month":"07"}