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 nite horizon contingent plan with highest probability of success within the horizon We describe two extensions of Graphplan in this direction The rst PGraphplan produces an optimal contingent plan It typically su ers a performance hit compared to Graphplan but still appears to be fast com pared with other approaches to probabilistic planning problems The second TGraphplan runs at essentially the same speed as Graphplan but produces potentially sub optimal policies TGraphplan s policy selects the rst action on the highest probability trajectory from its current state to the goal Ideally we would like an optimal planner for probabilistic domains with the same speed that Graphplan would have if the domain were made deterministic By comparing the speed and quality of these two planners to each other and to other existing planners we are able to estimate how far o we are from our ideal PGraphplan is based on a forward chaining search unlike the backward chaining search of the standard Graphplan algorithm Thus one focus of this paper is exploring the extent to which Graphplan s representation can be used to speed up forward search in addition to the backward search for which it was originally intended