Using Classical Planners to Solve Conformant Probabilistic Planning Problems

Motivated by the success of the translation-based approach for conformant planning, introduced by Palacios and Geffner,  we present two variants of a new compilation scheme from conformant probabilistic planning problems (CPP) to variants of classicalplanning.In CPP, we are given a set of actions -- which we assume to be deterministic in this paper, a distribution over initial states, a goal condition, and a value $0<p\leq 1$. Our task is to find a plan $\pi$ such that the goal probability following the execution of $\pi$ in the initial state is at least $p$. Our firstvariant translates CPP into classicalplanning with resource constraints, in which the resource represents probabilities of failure.  The second variant translates CPPinto cost-optimal classical planning problems, in which costs represents probabilities. Empirically, these techniques show mixed results, performing very well on some domains, and poorly on others. This  indicates that compilation-based technique are a feasible and promising direction for solving CPP problems and, possibly, more general probabilistic planning problems.