{"conference":{"name":"IAAI: Innovative Applications of Artificial Intelligence","location":"Austin, TX, USA","start_date":"2015-01-25","end_date":"2015-01-30"},"publication":"Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence ","external_id":{"isi":["000372683700002"],"arxiv":["1411.3880"]},"scopus_import":"1","project":[{"call_identifier":"FWF","name":"Modern Graph Algorithmic Techniques in Formal Verification","_id":"2584A770-B435-11E9-9278-68D0E5697425","grant_number":"P 23499-N23"},{"call_identifier":"FWF","_id":"25832EC2-B435-11E9-9278-68D0E5697425","grant_number":"S 11407_N23","name":"Rigorous Systems Engineering"},{"call_identifier":"FP7","name":"Quantitative Graph Games: Theory and Applications","_id":"2581B60A-B435-11E9-9278-68D0E5697425","grant_number":"279307"}],"month":"06","user_id":"317138e5-6ab7-11ef-aa6d-ffef3953e345","corr_author":"1","publist_id":"5286","type":"conference","publisher":"AAAI Press","date_published":"2015-06-01T00:00:00Z","author":[{"first_name":"Krishnendu","full_name":"Chatterjee, Krishnendu","last_name":"Chatterjee","orcid":"0000-0002-4561-241X","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87"},{"full_name":"Chmelik, Martin","first_name":"Martin","last_name":"Chmelik","id":"3624234E-F248-11E8-B48F-1D18A9856A87"},{"last_name":"Gupta","full_name":"Gupta, Raghav","first_name":"Raghav"},{"last_name":"Kanodia","full_name":"Kanodia, Ayush","first_name":"Ayush"}],"ec_funded":1,"title":"Optimal cost almost-sure reachability in POMDPs","language":[{"iso":"eng"}],"publication_status":"published","volume":5,"page":"3496-3502","status":"public","intvolume":"         5","day":"01","oa_version":"Preprint","oa":1,"article_processing_charge":"No","citation":{"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.","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.","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.","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.","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.","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."},"date_updated":"2025-09-18T11:05:08Z","department":[{"_id":"KrCh"}],"related_material":{"record":[{"id":"1529","status":"public","relation":"later_version"}]},"year":"2015","quality_controlled":"1","abstract":[{"lang":"eng","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."}],"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.","_id":"1820","arxiv":1,"date_created":"2018-12-11T11:54:11Z","main_file_link":[{"open_access":"1","url":"http://arxiv.org/abs/1411.3880"}],"alternative_title":["Artifical Intelligence"],"isi":1}