{"date_published":"2015-04-01T00:00:00Z","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","oa":1,"year":"2015","status":"public","publisher":"Elsevier","page":"46 - 72","external_id":{"arxiv":["1408.2058"]},"day":"01","citation":{"ista":"Chatterjee K, Chmelik M. 2015. POMDPs under probabilistic semantics. Artificial Intelligence. 221, 46–72.","chicago":"Chatterjee, Krishnendu, and Martin Chmelik. “POMDPs under Probabilistic Semantics.” <i>Artificial Intelligence</i>. Elsevier, 2015. <a href=\"https://doi.org/10.1016/j.artint.2014.12.009\">https://doi.org/10.1016/j.artint.2014.12.009</a>.","apa":"Chatterjee, K., &#38; Chmelik, M. (2015). POMDPs under probabilistic semantics. <i>Artificial Intelligence</i>. Elsevier. <a href=\"https://doi.org/10.1016/j.artint.2014.12.009\">https://doi.org/10.1016/j.artint.2014.12.009</a>","short":"K. Chatterjee, M. Chmelik, Artificial Intelligence 221 (2015) 46–72.","ama":"Chatterjee K, Chmelik M. POMDPs under probabilistic semantics. <i>Artificial Intelligence</i>. 2015;221:46-72. doi:<a href=\"https://doi.org/10.1016/j.artint.2014.12.009\">10.1016/j.artint.2014.12.009</a>","mla":"Chatterjee, Krishnendu, and Martin Chmelik. “POMDPs under Probabilistic Semantics.” <i>Artificial Intelligence</i>, vol. 221, Elsevier, 2015, pp. 46–72, doi:<a href=\"https://doi.org/10.1016/j.artint.2014.12.009\">10.1016/j.artint.2014.12.009</a>.","ieee":"K. Chatterjee and M. Chmelik, “POMDPs under probabilistic semantics,” <i>Artificial Intelligence</i>, vol. 221. Elsevier, pp. 46–72, 2015."},"month":"04","date_created":"2018-12-11T11:54:28Z","department":[{"_id":"KrCh"}],"publication_status":"published","publication":"Artificial Intelligence","abstract":[{"lang":"eng","text":"We consider partially observable Markov decision processes (POMDPs) with limit-average payoff, where a reward value in the interval [0,1] is associated with every transition, and the payoff of an infinite path is the long-run average of the rewards. We consider two types of path constraints: (i) a quantitative constraint defines the set of paths where the payoff is at least a given threshold λ1ε(0,1]; and (ii) a qualitative constraint which is a special case of the quantitative constraint with λ1=1. We consider the computation of the almost-sure winning set, where the controller needs to ensure that the path constraint is satisfied with probability 1. Our main results for qualitative path constraints are as follows: (i) the problem of deciding the existence of a finite-memory controller is EXPTIME-complete; and (ii) the problem of deciding the existence of an infinite-memory controller is undecidable. For quantitative path constraints we show that the problem of deciding the existence of a finite-memory controller is undecidable. We also present a prototype implementation of our EXPTIME algorithm and experimental results on several examples."}],"oa_version":"Preprint","main_file_link":[{"url":"https://arxiv.org/abs/1408.2058","open_access":"1"}],"_id":"1873","author":[{"id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","last_name":"Chatterjee","orcid":"0000-0002-4561-241X","first_name":"Krishnendu","full_name":"Chatterjee, Krishnendu"},{"last_name":"Chmelik","id":"3624234E-F248-11E8-B48F-1D18A9856A87","full_name":"Chmelik, Martin","first_name":"Martin"}],"doi":"10.1016/j.artint.2014.12.009","scopus_import":1,"quality_controlled":"1","title":"POMDPs under probabilistic semantics","intvolume":"       221","language":[{"iso":"eng"}],"date_updated":"2021-01-12T06:53:46Z","publist_id":"5224","volume":221,"type":"journal_article"}