Sequential Monte Carlo in reachability heuristics for probabilistic planning

The current best conformant probabilistic planners encode the problem as a bounded length CSP or SAT problem. While these approaches can find optimal solutions for given plan lengths, they often do not scale for large problems or plan lengths. As has been shown in classical planning, heuristic search outperforms CSP/SAT techniques (especially when a plan length is not given a priori). The problem with applying heuristic search in probabilistic planning is that effective heuristics are as yet lacking. In this work, we apply heuristic search to conformant probabilistic planning by adapting planning graph heuristics developed for non-deterministic planning. We evaluate a straightforward application of these planning graph techniques, which amounts to exactly computing the distribution over reachable relaxed planning graph layers. Computing these distributions is costly, so we apply Sequential Monte Carlo (SMC) to approximate them. A key issue we explore within SMC is how to automatically...

• Type: Journal
• Year: 2008
• Where: AI
• Authors: Daniel Bryce, Subbarao Kambhampati, David E. Smith
• Keywords: AI 2008, Artificial Intelligence, Heuristic Search, Heuristics, Planning