Probabilistic Planning in the Graphplan Framework

13 years 9 months ago
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.
Avrim Blum, John Langford
Added 04 Aug 2010
Updated 04 Aug 2010
Type Conference
Year 1999
Where ECP
Authors Avrim Blum, John Langford
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