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Iain Little

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ICAPS Conference 2006 Conference Paper

Concurrent Probabilistic Planning in the Graphplan Framework

Iain Little
Sylvie Thiébaux

We consider the problem of planning optimally in potentially concurrent probabilistic domains: actions have probabilistic effects and may execute in parallel under certain conditions; we seek a contingency plan that maximises the probability of reaching the goal. The Graphplan framework has proven to be highly successful at solving classical planning problems, but has not previously been applied to probabilistic planning in its entirety. We present an extension of the full framework to probabilistic domains that demonstrates a method of efficiently finding optimal contingency plans using a goal regression search. Paragraph, the resulting planner, is competitive with the state of the art, producing acyclic or cyclic plans that optionally exploit a problem's potential for concurrency.

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AAAI Conference 2005 Conference Paper

Prottle: A Probabilistic Temporal Planner

Iain Little

Planning with concurrent durative actions and probabilistic effects, or probabilistic temporal planning, is a relatively new area of research. The challenge is to replicate the success of modern temporal and probabilistic planners with domains that exhibit an interaction between time and uncertainty. We present a general framework for probabilistic temporal planning in which effects, the time at which they occur, and action durations are all probabilistic. This framework includes a search space that is designed for solving probabilistic temporal planning problems via heuristic search, an algorithm that has been tailored to work with it, and an effective heuristic based on an extension of the planning graph data structure. Prottle is a planner that implements this framework, and can solve problems expressed in an extension of PDDL.

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