Landmark-based heuristic online contingent planning

Shlomi Maliah, Guy Shani, Ronen I. Brafman

Research output: Contribution to journalArticlepeer-review

Abstract

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. We propose a heuristic, online method for contingent planning which focuses on identifying the next useful sensing action. We select the next sensing action based on a landmark heuristic, adapted from classical planning. We discuss landmarks for plan trees, providing several alternative definitions and discussing their merits. The key part of our planner is the novel landmarks-based heuristic, together with a projection method that uses classical planning to solve the intermediate conformant planning problems. The resulting heuristic contingent planner solves many more problems than state-of-the-art, translation-based online contingent planners, and in most cases, much faster, up to 3 times faster on simple problems, and 200 times faster on non-simple domains.

Original languageEnglish
Pages (from-to)602-634
Number of pages33
JournalAutonomous Agents and Multi-Agent Systems
Volume32
Issue number5
DOIs
StatePublished - 1 Sep 2018

Keywords

  • Automated planning
  • Belief space
  • Contingent planning
  • Landmarks
  • Online planning
  • Partial observability
  • Regression

ASJC Scopus subject areas

  • Artificial Intelligence

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