--- res: bibo_abstract: - '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.@eng' bibo_authorlist: - foaf_Person: foaf_givenName: Karel foaf_name: Horák, Karel foaf_surname: Horák - foaf_Person: foaf_givenName: Branislav foaf_name: Bošanský, Branislav foaf_surname: Bošanský - foaf_Person: foaf_givenName: Krishnendu foaf_name: Chatterjee, Krishnendu foaf_surname: Chatterjee foaf_workInfoHomepage: http://www.librecat.org/personId=2E5DCA20-F248-11E8-B48F-1D18A9856A87 orcid: 0000-0002-4561-241X bibo_doi: 10.24963/ijcai.2018/662 bibo_volume: 2018-July dct_date: 2018^xs_gYear dct_identifier: - UT:000764175404127 dct_language: eng dct_publisher: IJCAI@ dct_title: 'Goal-HSVI: Heuristic search value iteration for goal-POMDPs@' ...