Computing Contingent Plans via Fully Observable Non-Deterministic Planning

Christian J. Muise, Vaishak Belle, Sheila A. McIlraith

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

Planning with sensing actions under partial observability is a computationally challenging problem that is fundamental to the realization of AI tasks in areas as diverse as robotics, game playing, and diagnostic problem solving. Recent work on generating plans for partially observable domains has advocated for online planning, claiming that offline plans are often too large to generate. Here we push the envelope on this challenging problem, proposing a technique for generating conditional (aka contingent) plans offline. The key to our planner's success is the reliance on state-of-the-art techniques for fully observable non-deterministic (FOND) planning. In particular, we use an existing compilation for converting a planning problem under partial observability and sensing to a FOND planning problem. With a modified FOND planner in hand, we are able to scale beyond previous techniques for generating conditional plans with solutions that are orders of magnitude smaller than previously possible in some domains.
Original languageEnglish
Title of host publicationProceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, July 27 -31, 2014, Québec City, Québec, Canada.
PublisherAAAI Press
Pages2322-2329
Number of pages8
Publication statusPublished - 2014

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  • Computing Contingent Plans via Fully Observable Non-Deterministic Planning

    Muise, C. J., McIlraith, S. A. & Belle, V., 2014, Proceedings of the 1st Workshop on Models and Paradigms for Planning under Uncertainty: a Broad Perspective. Kolobov, A., Kuter, U. & Teichteil-Königsbuch, F. (eds.). Portsmouth, New Hampshire, USA, p. 27-34 8 p.

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Open Access

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