{"date_updated":"2023-02-23T12:23:59Z","publist_id":"3276","alternative_title":["LNCS"],"oa_version":"Preprint","conference":{"start_date":"2011-08-22","location":"Warsaw, Poland","end_date":"2011-08-26","name":"MFCS: Mathematical Foundations of Computer Science"},"author":[{"full_name":"Chatterjee, Krishnendu","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-4561-241X","first_name":"Krishnendu","last_name":"Chatterjee"},{"first_name":"Laurent","last_name":"Doyen","full_name":"Doyen, Laurent"}],"external_id":{"arxiv":["1104.2909"]},"status":"public","type":"conference","page":"206 - 218","doi":"10.1007/978-3-642-22993-0_21","date_published":"2011-09-28T00:00:00Z","citation":{"chicago":"Chatterjee, Krishnendu, and Laurent Doyen. “Energy and Mean-Payoff Parity Markov Decision Processes,” 6907:206–18. Springer, 2011. <a href=\"https://doi.org/10.1007/978-3-642-22993-0_21\">https://doi.org/10.1007/978-3-642-22993-0_21</a>.","apa":"Chatterjee, K., &#38; Doyen, L. (2011). Energy and mean-payoff parity Markov Decision Processes (Vol. 6907, pp. 206–218). Presented at the MFCS: Mathematical Foundations of Computer Science, Warsaw, Poland: Springer. <a href=\"https://doi.org/10.1007/978-3-642-22993-0_21\">https://doi.org/10.1007/978-3-642-22993-0_21</a>","ista":"Chatterjee K, Doyen L. 2011. Energy and mean-payoff parity Markov Decision Processes. MFCS: Mathematical Foundations of Computer Science, LNCS, vol. 6907, 206–218.","short":"K. Chatterjee, L. Doyen, in:, Springer, 2011, pp. 206–218.","mla":"Chatterjee, Krishnendu, and Laurent Doyen. <i>Energy and Mean-Payoff Parity Markov Decision Processes</i>. Vol. 6907, Springer, 2011, pp. 206–18, doi:<a href=\"https://doi.org/10.1007/978-3-642-22993-0_21\">10.1007/978-3-642-22993-0_21</a>.","ama":"Chatterjee K, Doyen L. Energy and mean-payoff parity Markov Decision Processes. In: Vol 6907. Springer; 2011:206-218. doi:<a href=\"https://doi.org/10.1007/978-3-642-22993-0_21\">10.1007/978-3-642-22993-0_21</a>","ieee":"K. Chatterjee and L. Doyen, “Energy and mean-payoff parity Markov Decision Processes,” presented at the MFCS: Mathematical Foundations of Computer Science, Warsaw, Poland, 2011, vol. 6907, pp. 206–218."},"intvolume":"      6907","project":[{"name":"Rigorous Systems Engineering","call_identifier":"FWF","grant_number":"S 11407_N23","_id":"25832EC2-B435-11E9-9278-68D0E5697425"},{"_id":"2587B514-B435-11E9-9278-68D0E5697425","name":"Microsoft Research Faculty Fellowship"}],"main_file_link":[{"open_access":"1","url":"http://arxiv.org/abs/1104.2909"}],"related_material":{"record":[{"relation":"earlier_version","status":"public","id":"5387"}]},"quality_controlled":"1","title":"Energy and mean-payoff parity Markov Decision Processes","year":"2011","month":"09","volume":6907,"abstract":[{"text":"We consider Markov Decision Processes (MDPs) with mean-payoff parity and energy parity objectives. In system design, the parity objective is used to encode ω-regular specifications, and the mean-payoff and energy objectives can be used to model quantitative resource constraints. The energy condition re- quires that the resource level never drops below 0, and the mean-payoff condi- tion requires that the limit-average value of the resource consumption is within a threshold. While these two (energy and mean-payoff) classical conditions are equivalent for two-player games, we show that they differ for MDPs. We show that the problem of deciding whether a state is almost-sure winning (i.e., winning with probability 1) in energy parity MDPs is in NP ∩ coNP, while for mean- payoff parity MDPs, the problem is solvable in polynomial time, improving a recent PSPACE bound.","lang":"eng"}],"date_created":"2018-12-11T12:02:48Z","language":[{"iso":"eng"}],"publication_status":"published","_id":"3345","oa":1,"scopus_import":1,"department":[{"_id":"KrCh"}],"publisher":"Springer","day":"28","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87"}