Planning in Stochastic Environments with Goal Uncertainty

We present the Goal Uncertain Stochastic Shortest Path (GUSSP) problem --- a general framework to model stochastic environments with goal uncertainty. The model is an extension of the stochastic shortest path (SSP) framework to dynamic environments in which it is impossible to determine the exact goal states ahead of plan execution. GUSSPs introduce flexibility in goal specification by allowing a belief over possible goal configurations. The partial observability is restricted to goals, facilitating the reduction to an SSP. We formally define a GUSSP and discuss its theoretical properties. We then propose an admissible heuristic that reduces the planning time of FLARES --- a start-of-the-art probabilistic planner. We also propose a determinization approach for solving this class of problems. Finally, we present empirical results using a mobile robot and three other problem domains.