{"oa":1,"day":"01","author":[{"last_name":"Tomášek","full_name":"Tomášek, Petr","first_name":"Petr"},{"last_name":"Horák","first_name":"Karel","full_name":"Horák, Karel"},{"last_name":"Aradhye","full_name":"Aradhye, Aditya","first_name":"Aditya"},{"first_name":"Branislav","full_name":"Bošanský, Branislav","last_name":"Bošanský"},{"full_name":"Chatterjee, Krishnendu","first_name":"Krishnendu","id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","orcid":"0000-0002-4561-241X","last_name":"Chatterjee"}],"article_processing_charge":"No","doi":"10.24963/ijcai.2021/575","_id":"10847","department":[{"_id":"KrCh"}],"type":"conference","status":"public","project":[{"name":"Formal Methods for Stochastic Models: Algorithms and Applications","_id":"0599E47C-7A3F-11EA-A408-12923DDC885E","call_identifier":"H2020","grant_number":"863818"}],"publication":"30th International Joint Conference on Artificial Intelligence","scopus_import":"1","citation":{"apa":"Tomášek, P., Horák, K., Aradhye, A., Bošanský, B., &#38; Chatterjee, K. (2021). Solving partially observable stochastic shortest-path games. In <i>30th International Joint Conference on Artificial Intelligence</i> (pp. 4182–4189). Virtual, Online: International Joint Conferences on Artificial Intelligence. <a href=\"https://doi.org/10.24963/ijcai.2021/575\">https://doi.org/10.24963/ijcai.2021/575</a>","short":"P. Tomášek, K. Horák, A. Aradhye, B. Bošanský, K. Chatterjee, in:, 30th International Joint Conference on Artificial Intelligence, International Joint Conferences on Artificial Intelligence, 2021, pp. 4182–4189.","chicago":"Tomášek, Petr, Karel Horák, Aditya Aradhye, Branislav Bošanský, and Krishnendu Chatterjee. “Solving Partially Observable Stochastic Shortest-Path Games.” In <i>30th International Joint Conference on Artificial Intelligence</i>, 4182–89. International Joint Conferences on Artificial Intelligence, 2021. <a href=\"https://doi.org/10.24963/ijcai.2021/575\">https://doi.org/10.24963/ijcai.2021/575</a>.","mla":"Tomášek, Petr, et al. “Solving Partially Observable Stochastic Shortest-Path Games.” <i>30th International Joint Conference on Artificial Intelligence</i>, International Joint Conferences on Artificial Intelligence, 2021, pp. 4182–89, doi:<a href=\"https://doi.org/10.24963/ijcai.2021/575\">10.24963/ijcai.2021/575</a>.","ieee":"P. Tomášek, K. Horák, A. Aradhye, B. Bošanský, and K. Chatterjee, “Solving partially observable stochastic shortest-path games,” in <i>30th International Joint Conference on Artificial Intelligence</i>, Virtual, Online, 2021, pp. 4182–4189.","ista":"Tomášek P, Horák K, Aradhye A, Bošanský B, Chatterjee K. 2021. Solving partially observable stochastic shortest-path games. 30th International Joint Conference on Artificial Intelligence. IJCAI: International Joint Conferences on Artificial Intelligence Organization, 4182–4189.","ama":"Tomášek P, Horák K, Aradhye A, Bošanský B, Chatterjee K. Solving partially observable stochastic shortest-path games. In: <i>30th International Joint Conference on Artificial Intelligence</i>. International Joint Conferences on Artificial Intelligence; 2021:4182-4189. doi:<a href=\"https://doi.org/10.24963/ijcai.2021/575\">10.24963/ijcai.2021/575</a>"},"page":"4182-4189","date_published":"2021-09-01T00:00:00Z","language":[{"iso":"eng"}],"user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_created":"2022-03-13T23:01:47Z","publication_status":"published","publisher":"International Joint Conferences on Artificial Intelligence","title":"Solving partially observable stochastic shortest-path games","conference":{"name":"IJCAI: International Joint Conferences on Artificial Intelligence Organization","location":"Virtual, Online","end_date":"2021-08-27","start_date":"2021-08-19"},"quality_controlled":"1","abstract":[{"text":"We study the two-player zero-sum extension of the partially observable stochastic shortest-path problem where one agent has only partial information about the environment. We formulate this problem as a partially observable stochastic game (POSG): given a set of target states and negative rewards for each transition, the player with imperfect information maximizes the expected undiscounted total reward until a target state is reached. The second player with the perfect information aims for the opposite. We base our formalism on POSGs with one-sided observability (OS-POSGs) and give the following contributions: (1) we introduce a novel heuristic search value iteration algorithm that iteratively solves depth-limited variants of the game, (2) we derive the bound on the depth guaranteeing an arbitrary precision, (3) we propose a novel upper-bound estimation that allows early terminations, and (4) we experimentally evaluate the algorithm on a pursuit-evasion game.","lang":"eng"}],"date_updated":"2022-08-05T09:05:06Z","ec_funded":1,"month":"09","publication_identifier":{"isbn":["9780999241196"],"issn":["1045-0823"]},"year":"2021","acknowledgement":"This research was supported by the Czech Science Foundation (no. 19-24384Y), by the OP VVV MEYS funded project CZ.02.1.01/0.0/0.0/16 019/0000765 “Research Center for Informatics”, by the ERC CoG 863818 (ForM-SMArt), and by the Combat Capabilities Development Command Army Research Laboratory and was accomplished under Cooperative\r\nAgreement Number W911NF-13-2-0045 (ARL Cyber Security CRA). The views and conclusions contained in this document are those of the authors and should not be interpreted as\r\nrepresenting the official policies, either expressed or implied, of the Combat Capabilities Development Command Army Research Laboratory or the U.S. Government. The U.S. Government is authorized to reproduce and distribute reprints for Government purposes not withstanding any copyright notation here on. ","main_file_link":[{"url":"https://doi.org/10.24963/ijcai.2021/575","open_access":"1"}],"oa_version":"Published Version"}