Concurrent Probabilistic Planning in the Graphplan Framework

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Concurrent Probabilistic Planning in the Graphplan Framework
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.
Iain Little, Sylvie Thiébaux
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Where AIPS
Authors Iain Little, Sylvie Thiébaux
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