{"publist_id":"5286","main_file_link":[{"url":"http://arxiv.org/abs/1411.3880","open_access":"1"}],"ec_funded":1,"date_updated":"2023-02-23T10:02:57Z","citation":{"ama":"Chatterjee K, Chmelik M, Gupta R, Kanodia A. Optimal cost almost-sure reachability in POMDPs. In: <i>Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence </i>. Vol 5. AAAI Press; 2015:3496-3502.","apa":"Chatterjee, K., Chmelik, M., Gupta, R., &#38; Kanodia, A. (2015). Optimal cost almost-sure reachability in POMDPs. In <i>Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence </i> (Vol. 5, pp. 3496–3502). Austin, TX, USA: AAAI Press.","ieee":"K. Chatterjee, M. Chmelik, R. Gupta, and A. Kanodia, “Optimal cost almost-sure reachability in POMDPs,” in <i>Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence </i>, Austin, TX, USA, 2015, vol. 5, pp. 3496–3502.","mla":"Chatterjee, Krishnendu, et al. “Optimal Cost Almost-Sure Reachability in POMDPs.” <i>Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence </i>, vol. 5, AAAI Press, 2015, pp. 3496–502.","chicago":"Chatterjee, Krishnendu, Martin Chmelik, Raghav Gupta, and Ayush Kanodia. “Optimal Cost Almost-Sure Reachability in POMDPs.” In <i>Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence </i>, 5:3496–3502. AAAI Press, 2015.","short":"K. Chatterjee, M. Chmelik, R. Gupta, A. Kanodia, in:, Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence , AAAI Press, 2015, pp. 3496–3502.","ista":"Chatterjee K, Chmelik M, Gupta R, Kanodia A. 2015. Optimal cost almost-sure reachability in POMDPs. Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence . IAAI: Innovative Applications of Artificial Intelligence, Artifical Intelligence, vol. 5, 3496–3502."},"project":[{"name":"Modern Graph Algorithmic Techniques in Formal Verification","call_identifier":"FWF","_id":"2584A770-B435-11E9-9278-68D0E5697425","grant_number":"P 23499-N23"},{"name":"Rigorous Systems Engineering","call_identifier":"FWF","grant_number":"S 11407_N23","_id":"25832EC2-B435-11E9-9278-68D0E5697425"},{"name":"Quantitative Graph Games: Theory and Applications","call_identifier":"FP7","grant_number":"279307","_id":"2581B60A-B435-11E9-9278-68D0E5697425"}],"type":"conference","intvolume":"         5","title":"Optimal cost almost-sure reachability in POMDPs","acknowledgement":" The research was partly supported by Austrian Science Fund (FWF) Grant No P23499-N23, FWF NFN Grant No S11407-N23 (RiSE), ERC Start grant (279307: Graph Games), and Microsoft faculty fellows award.","alternative_title":["Artifical Intelligence"],"conference":{"start_date":"2015-01-25","location":"Austin, TX, USA","name":"IAAI: Innovative Applications of Artificial Intelligence","end_date":"2015-01-30"},"department":[{"_id":"KrCh"}],"year":"2015","quality_controlled":"1","abstract":[{"text":"We consider partially observable Markov decision processes (POMDPs) with a set of target states and every transition is associated with an integer cost. The optimization objec- tive we study asks to minimize the expected total cost till the target set is reached, while ensuring that the target set is reached almost-surely (with probability 1). We show that for integer costs approximating the optimal cost is undecidable. For positive costs, our results are as follows: (i) we establish matching lower and upper bounds for the optimal cost and the bound is double exponential; (ii) we show that the problem of approximating the optimal cost is decidable and present ap- proximation algorithms developing on the existing algorithms for POMDPs with finite-horizon objectives. While the worst- case running time of our algorithm is double exponential, we present efficient stopping criteria for the algorithm and show experimentally that it performs well in many examples.","lang":"eng"}],"oa":1,"volume":5,"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","scopus_import":1,"date_created":"2018-12-11T11:54:11Z","status":"public","day":"01","_id":"1820","publication":"Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence ","page":"3496-3502","external_id":{"arxiv":["1411.3880"]},"language":[{"iso":"eng"}],"related_material":{"record":[{"id":"1529","status":"public","relation":"later_version"}]},"oa_version":"Preprint","publisher":"AAAI Press","author":[{"full_name":"Chatterjee, Krishnendu","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-4561-241X","first_name":"Krishnendu","last_name":"Chatterjee"},{"last_name":"Chmelik","full_name":"Chmelik, Martin","id":"3624234E-F248-11E8-B48F-1D18A9856A87","first_name":"Martin"},{"full_name":"Gupta, Raghav","first_name":"Raghav","last_name":"Gupta"},{"last_name":"Kanodia","full_name":"Kanodia, Ayush","first_name":"Ayush"}],"publication_status":"published","date_published":"2015-06-01T00:00:00Z","month":"06"}