Hindsight Optimization for Probabilistic Planning with Factored Actions

Inspired by the success of the satisfiability approach for deterministic planning, we propose a novel framework for on-line stochastic planning, by embedding the idea of hindsight optimization into a reduction to integer linear programming. In contrast to the previous work using reductions or hindsight optimization, our formulation is general purpose by working with domain specifications over factored state and action spaces, and by doing so is also scalable in principle to exponentially large action spaces. Our approach is competitive with state-of-theart stochastic planners on challenging benchmark problems, and sometimes exceeds their performance especially in large action spaces.