Probabilistic Planning via Determinization in Hindsight

This paper investigates hindsight optimization as an approach for leveraging the significant advances in deterministic planning for action selection in probabilistic domains. Hindsight optimization is an online technique that evaluates the onestep-reachable states by sampling future outcomes to generate multiple non-stationary deterministic planning problems which can then be solved using search. Hindsight optimization has been successfully used in a number of online scheduling applications; however, it has not yet been considered in the substantially different context of goal-based probabilistic planning. We describe an implementation of hindsight optimization for probabilistic planning based on deterministic forward heuristic search and evaluate its performance on planning-competition benchmarks and other probabilistically interesting problems. The planner is able to outperform a number of probabilistic planners including FFReplan on many problems. Finally, we investigate conditions...

• Type: Conference
• Year: 2008
• Where: AAAI
• Authors: Sung Wook Yoon, Alan Fern, Robert Givan, Subbarao Kambhampati
• Keywords: AAAI 2008, Deterministic Planning, Hindsight Optimization, Intelligent Agents, Probabilistic