Directed fixed-point regression-based planning for non-deterministic domains

We present a novel approach to fully-observable nondeterministic planning (FOND) that attempts to bridge the gap between symbolic fix-point computation and recent approaches based on forward heuristic search. Concretely, we formalize the relationship between symbolic and dynamic programming nondeterministic planners, and then exploit such connection to propose a novel family of planning algorithms that reasons over symbolic policies in a directed manner. By doing so, our proposal reasons over sets of states and executions in a succinct way (as done by symbolic planners) while biasing the reasoning with respect to the initial and goal states of the specific planning problem at hand (as done by heuristic planners). We show empirical results that prove this approach promising in settings where there is an intrinsic tension between plan efficiency and plan "robustness," a feature to be expected in nondeterministic domains