Adaptive Outcome Selection for Planning with Reduced Models Reduced models allow autonomous robots to cope with the complexity of planning in stochastic environments by simplifying the model and reducing its accuracy. The solution quality of a reduced model depends on its fidelity. We present 01 reduced model that selectively improves model fidelity in certain states by switching between using a simplified deterministic model and the full model, without significantly compromising the run time gains. We measure the reduction impact for a reduced model based on the values of the ignored outcomes and use this as a heuristic for outcome selection. Finally, we present empirical results of our approach on three different domains, including an electric vehicle charging problem using realworld data from a university campus.

Planning in Stochastic Environments with Goal Uncertainty We present the Goal Uncertain Stochastic Shortest Path (GUSSP) problem xe2x80x94 a general framework to model path planning and decision making in stochastic environments with goal uncertainty. The framework extends the stochastic shortest path (SSP) model 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 unique observations at potential goals helps the agent identify the true goal during plan execution. The partial observability is restricted to goals, facilitating the reduction to an SSP with a modified state space. We formally define a GUSSP and discuss its theoretical properties. We then propose an admissible heuristic that reduces the planning time using FLARES xe2x80x94 a startoftheart probabilistic planner. We also propose a determinization approach for solving this class of problems. Finally, we present empirical results on a search and rescue mobile robot and three other problem domains in simulation.