Computing Contingent Plans via Fully Observable Non-Deterministic Planning

C. J. Muise, S. A. McIlraith, V. Belle

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

Abstract

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 1st Workshop on Models and Paradigms for Planning under Uncertainty: a Broad Perspective
EditorsAndrey Kolobov, Ugur Kuter, Florent Teichteil-Königsbuch
Place of PublicationPortsmouth, New Hampshire, USA
Pages27-34
Number of pages8
Publication statusPublished - 2014
Event24th International Conference on Automated Planning and Scheduling - Portsmouth, United States
Duration: 21 Jun 201426 Jun 2014
http://icaps14.icaps-conference.org/index/

Conference

Conference24th International Conference on Automated Planning and Scheduling
Abbreviated titleICAPS 2014
CountryUnited States
CityPortsmouth
Period21/06/1426/06/14
Internet address

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