File(s) stored somewhere else
Please note: Linked content is NOT stored on Carnegie Mellon University and we can't guarantee its availability, quality, security or accept any liability.
Guided Symbolic Universal Planning
Any type of content formally published in an academic journal, usually following a peer-review process.
Symbolic universal planning based on the reduced Ordered Binary Decision Diagram (OBDD) has been shown to be an efficient approach for planning in non-deterministic domains. To date, however, no guided algorithms exist for synthesizing universal plans. In this paper, we introduce a general approach for guiding universal planning based on an existing method for heuristic symbolic search in deterministic domains. We present three new sound and complete algorithms for best-first strong, strong cyclic, and weak universal planning. Our experimental results show that guiding the search dramatically can reduce both the computation time and the size of the generated plans.