*Nicholas Kushmerick, Steve Hanks, Daniel Weld*

We define the probabilistic planning problem in terms of a probability distribution over initial world states, a boolean combination of goal propositions, a probability threshold, and actions whose effects depend on the execution-time state of the world and on random chance. Adopting a probabilistic model complicates the definition of plan success: instead of demanding a plan that proovably achieves the goal, we seek plans whose probability of success exceeds the threshold. This paper describes a probabilistic semantics for planning under uncertainty, and presents a fully implemented algorithm that generates plans that succeed with probability no less than a user-supplied probability threshold. The algorithm is sound (if it terminates then the generated plan is sufficiently likely to achieve the goal) and complete (the algorithm will generate a solution if one exists).

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