Partially observable online contingent planning using landmark heuristics

Shlomi Maliah, Ronen Brafman, Erez Karpas, Guy Shani

Research output: Contribution to journalConference articlepeer-review

21 Scopus citations


In contingent planning problems, agents have partial information about their state and use sensing actions to learn the value of some variables. When sensing and actuation are separated, plans for such problems can often be viewed as a tree of sensing actions, separated by conformant plans consisting of non-sensing actions that enable the execution of the next sensing action. This leads us to propose a heuristic, online method for contingent planning which focuses on identifying the next useful sensing action. The key part of our planner is a novel landmarks-based heuristic for selecting the next sensing action, together with a projection method that uses classical planning to solve the intermediate conformant planning problems. This allows our planner to operate without an explicit model of belief space or the use of existing translation techniques, both of which can require exponential space. The resulting Heuristic Contingent Planner (HCP) solves many more problems than state-of-the-art, translation-based online contingent planners, and in most cases much faster.

Original languageEnglish
Pages (from-to)163-171
Number of pages9
JournalProceedings International Conference on Automated Planning and Scheduling, ICAPS
Issue numberJanuary
StatePublished - 1 Jan 2014
Event24th International Conference on Automated Planning and Scheduling, ICAPS 2014 - Portsmouth, United States
Duration: 21 Jun 201426 Jun 2014

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems and Management


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