[{"language":[{"iso":"eng"}],"volume":"2018-July","department":[{"_id":"KrCh"}],"date_published":"2018-07-01T00:00:00Z","external_id":{"isi":["000764175404127"]},"publication":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","day":"01","ec_funded":1,"article_processing_charge":"No","conference":{"end_date":"2018-07-19","location":"Stockholm, Sweden","start_date":"2018-07-13","name":"IJCAI: International Joint Conference on Artificial Intelligence"},"acknowledgement":"∗This work has been supported by Vienna Science and Technology Fund (WWTF) Project ICT15-003, Austrian Science Fund (FWF) NFN Grant No S11407-N23 (RiSE/SHiNE), and ERC Starting grant (279307: Graph Games). This research was sponsored by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-13-2-0045 (ARL Cyber Security CRA). ","date_updated":"2025-04-14T13:51:04Z","publist_id":"8030","oa":1,"citation":{"mla":"Horák, Karel, et al. “Goal-HSVI: Heuristic Search Value Iteration for Goal-POMDPs.” <i>Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence</i>, vol. 2018–July, IJCAI, 2018, pp. 4764–70, doi:<a href=\"https://doi.org/10.24963/ijcai.2018/662\">10.24963/ijcai.2018/662</a>.","ieee":"K. Horák, B. Bošanský, and K. Chatterjee, “Goal-HSVI: Heuristic search value iteration for goal-POMDPs,” in <i>Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence</i>, Stockholm, Sweden, 2018, vol. 2018–July, pp. 4764–4770.","chicago":"Horák, Karel, Branislav Bošanský, and Krishnendu Chatterjee. “Goal-HSVI: Heuristic Search Value Iteration for Goal-POMDPs.” In <i>Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence</i>, 2018–July:4764–70. IJCAI, 2018. <a href=\"https://doi.org/10.24963/ijcai.2018/662\">https://doi.org/10.24963/ijcai.2018/662</a>.","apa":"Horák, K., Bošanský, B., &#38; Chatterjee, K. (2018). Goal-HSVI: Heuristic search value iteration for goal-POMDPs. In <i>Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence</i> (Vol. 2018–July, pp. 4764–4770). Stockholm, Sweden: IJCAI. <a href=\"https://doi.org/10.24963/ijcai.2018/662\">https://doi.org/10.24963/ijcai.2018/662</a>","ista":"Horák K, Bošanský B, Chatterjee K. 2018. Goal-HSVI: Heuristic search value iteration for goal-POMDPs. Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence. IJCAI: International Joint Conference on Artificial Intelligence vol. 2018–July, 4764–4770.","short":"K. Horák, B. Bošanský, K. Chatterjee, in:, Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, IJCAI, 2018, pp. 4764–4770.","ama":"Horák K, Bošanský B, Chatterjee K. Goal-HSVI: Heuristic search value iteration for goal-POMDPs. In: <i>Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence</i>. Vol 2018-July. IJCAI; 2018:4764-4770. doi:<a href=\"https://doi.org/10.24963/ijcai.2018/662\">10.24963/ijcai.2018/662</a>"},"type":"conference","abstract":[{"text":"Partially observable Markov decision processes (POMDPs) are the standard models for planning under uncertainty with both finite and infinite horizon. Besides the well-known discounted-sum objective, indefinite-horizon objective (aka Goal-POMDPs) is another classical objective for POMDPs. In this case, given a set of target states and a positive cost for each transition, the optimization objective is to minimize the expected total cost until a target state is reached. In the literature, RTDP-Bel or heuristic search value iteration (HSVI) have been used for solving Goal-POMDPs. Neither of these algorithms has theoretical convergence guarantees, and HSVI may even fail to terminate its trials. We give the following contributions: (1) We discuss the challenges introduced in Goal-POMDPs and illustrate how they prevent the original HSVI from converging. (2) We present a novel algorithm inspired by HSVI, termed Goal-HSVI, and show that our algorithm has convergence guarantees. (3) We show that Goal-HSVI outperforms RTDP-Bel on a set of well-known examples.","lang":"eng"}],"date_created":"2018-12-11T11:44:13Z","scopus_import":"1","oa_version":"Published Version","status":"public","main_file_link":[{"url":"https://doi.org/10.24963/ijcai.2018/662","open_access":"1"}],"month":"07","author":[{"full_name":"Horák, Karel","first_name":"Karel","last_name":"Horák"},{"first_name":"Branislav","full_name":"Bošanský, Branislav","last_name":"Bošanský"},{"id":"2E5DCA20-F248-11E8-B48F-1D18A9856A87","full_name":"Chatterjee, Krishnendu","first_name":"Krishnendu","last_name":"Chatterjee","orcid":"0000-0002-4561-241X"}],"project":[{"name":"Efficient Algorithms for Computer Aided Verification","_id":"25892FC0-B435-11E9-9278-68D0E5697425","grant_number":"ICT15-003"},{"name":"Rigorous Systems Engineering","_id":"25832EC2-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","grant_number":"S 11407_N23"},{"_id":"2581B60A-B435-11E9-9278-68D0E5697425","call_identifier":"FP7","grant_number":"279307","name":"Quantitative Graph Games: Theory and Applications"}],"publication_status":"published","quality_controlled":"1","_id":"25","page":"4764 - 4770","year":"2018","isi":1,"title":"Goal-HSVI: Heuristic search value iteration for goal-POMDPs","publisher":"IJCAI","doi":"10.24963/ijcai.2018/662","user_id":"c635000d-4b10-11ee-a964-aac5a93f6ac1"}]