{"project":[{"name":"Efficient Algorithms for Computer Aided Verification","_id":"25892FC0-B435-11E9-9278-68D0E5697425","grant_number":"ICT15-003"},{"call_identifier":"FWF","grant_number":"S 11407_N23","_id":"25832EC2-B435-11E9-9278-68D0E5697425","name":"Rigorous Systems Engineering"},{"call_identifier":"FP7","grant_number":"279307","name":"Quantitative Graph Games: Theory and Applications","_id":"2581B60A-B435-11E9-9278-68D0E5697425"}],"volume":2018,"isi":1,"external_id":{"arxiv":["1804.08984"],"isi":["000764175404118"]},"intvolume":" 2018","date_updated":"2024-06-29T22:30:40Z","abstract":[{"lang":"eng","text":"We consider the stochastic shortest path (SSP)problem for succinct Markov decision processes(MDPs), where the MDP consists of a set of vari-ables, and a set of nondeterministic rules that up-date the variables. First, we show that several ex-amples from the AI literature can be modeled assuccinct MDPs. Then we present computationalapproaches for upper and lower bounds for theSSP problem: (a) for computing upper bounds, ourmethod is polynomial-time in the implicit descrip-tion of the MDP; (b) for lower bounds, we present apolynomial-time (in the size of the implicit descrip-tion) reduction to quadratic programming. Our ap-proach is applicable even to infinite-state MDPs.Finally, we present experimental results to demon-strate the effectiveness of our approach on severalclassical examples from the AI literature."}],"status":"public","day":"17","quality_controlled":"1","main_file_link":[{"url":"https://arxiv.org/abs/1804.08984","open_access":"1"}],"related_material":{"record":[{"status":"public","id":"8934","relation":"dissertation_contains"}]},"ec_funded":1,"publisher":"IJCAI","oa":1,"scopus_import":"1","conference":{"name":"IJCAI: International Joint Conference on Artificial Intelligence","end_date":"2018-07-19","start_date":"2018-07-13","location":"Stockholm, Sweden"},"publication_status":"published","_id":"5977","publication_identifier":{"issn":["10450823"],"isbn":["978-099924112-7"]},"doi":"10.24963/ijcai.2018/653","title":"Computational approaches for stochastic shortest path on succinct MDPs","month":"07","oa_version":"Preprint","type":"conference","citation":{"apa":"Chatterjee, K., Fu, H., Goharshady, A. K., & Okati, N. (2018). Computational approaches for stochastic shortest path on succinct MDPs. In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence (Vol. 2018, pp. 4700–4707). Stockholm, Sweden: IJCAI. https://doi.org/10.24963/ijcai.2018/653","short":"K. Chatterjee, H. Fu, A.K. Goharshady, N. Okati, in:, Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI, 2018, pp. 4700–4707.","ieee":"K. Chatterjee, H. Fu, A. K. Goharshady, and N. Okati, “Computational approaches for stochastic shortest path on succinct MDPs,” in Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 2018, vol. 2018, pp. 4700–4707.","mla":"Chatterjee, Krishnendu, et al. “Computational Approaches for Stochastic Shortest Path on Succinct MDPs.” Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, vol. 2018, IJCAI, 2018, pp. 4700–07, doi:10.24963/ijcai.2018/653.","chicago":"Chatterjee, Krishnendu, Hongfei Fu, Amir Kafshdar Goharshady, and Nastaran Okati. “Computational Approaches for Stochastic Shortest Path on Succinct MDPs.” In Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018:4700–4707. IJCAI, 2018. https://doi.org/10.24963/ijcai.2018/653.","ista":"Chatterjee K, Fu H, Goharshady AK, Okati N. 2018. Computational approaches for stochastic shortest path on succinct MDPs. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. IJCAI: International Joint Conference on Artificial Intelligence vol. 2018, 4700–4707.","ama":"Chatterjee K, Fu H, Goharshady AK, Okati N. Computational approaches for stochastic shortest path on succinct MDPs. In: Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. Vol 2018. IJCAI; 2018:4700-4707. doi:10.24963/ijcai.2018/653"},"publication":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","date_created":"2019-02-13T13:26:27Z","year":"2018","article_processing_charge":"No","date_published":"2018-07-17T00:00:00Z","language":[{"iso":"eng"}],"author":[{"first_name":"Krishnendu","orcid":"0000-0002-4561-241X","full_name":"Chatterjee, Krishnendu","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","last_name":"Chatterjee"},{"first_name":"Hongfei","full_name":"Fu, Hongfei","last_name":"Fu","id":"3AAD03D6-F248-11E8-B48F-1D18A9856A87"},{"first_name":"Amir","orcid":"0000-0003-1702-6584","full_name":"Goharshady, Amir","id":"391365CE-F248-11E8-B48F-1D18A9856A87","last_name":"Goharshady"},{"last_name":"Okati","first_name":"Nastaran","full_name":"Okati, Nastaran"}],"user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1","page":"4700-4707","department":[{"_id":"KrCh"}]}