Probabilistic Planning in the Graphplan Framework
The Graphplan planner has enjoyed considerable success as a planning algorithm for classical STRIPS domains. In this paper we explore the extent to which its representation can be used for probabilistic planning. In particular, we consider an MDP-style framework in which the state of the world is known but actions are probabilistic, and the objective is to produce a finite horizon contingent plan with highest probability of success within the horizon.