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Planning with durative actions in stochastic domains

Published: 01 January 2008 Publication History

Abstract

Probabilistic planning problems are typically modeled as a Markov Decision Process (MDP). MDPs, while an otherwise expressive model, allow only for sequential, non-durative actions. This poses severe restrictions in modeling and solving a real world planning problem. We extend the MDP model to incorporate-1) simultaneous action execution, 2) durative actions, and 3) stochastic durations. We develop several algorithms to combat the computational explosion introduced by these features. The key theoretical ideas used in building these algorithms are -- modeling a complex problem as an MDP in extended state/action space, pruning of irrelevant actions, sampling of relevant actions, using informed heuristics to guide the search, hybridizing different planners to achieve benefits of both, approximating the problem and replanning. Our empirical evaluation illuminates the different merits in using various algorithms, viz., optimality, empirical closeness to optimality, theoretical error bounds, and speed.

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Published In

cover image Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research  Volume 31, Issue 1
January 2008
651 pages

Publisher

AI Access Foundation

El Segundo, CA, United States

Publication History

Published: 01 January 2008
Received: 01 February 2007
Published in JAIR Volume 31, Issue 1

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  • (2017)Multirobot Symbolic Planning under Temporal UncertaintyProceedings of the 16th Conference on Autonomous Agents and MultiAgent Systems10.5555/3091125.3091199(501-510)Online publication date: 8-May-2017
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