---
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@'
...