Faster Probabilistic Planning Through More Efficient Stochastic Satisfiability Problem Encodings

Proceedings Of The Sixth International Conference On Artificial Intelligence Planning And Scheduling

Volume

Proceedings Of The Sixth International Conference On Artificial Intelligence Planning And Scheduling

Contents

The propositional contingent planner ZANDER solves finitehorizon, partially observable, probabilistic planning problems at state-of-the-art-speeds by converting the planning problem to a stochastic satisfiability (SSAT) problem and solving that problem instead (Majercik 2000). ZANDER obtains these results using a relatively inefficient SSAT encoding of the problem (a linear action encoding with classical frame axioms). We describe and analyze three alternative SSAT encodings for probabilistic planning problems: a linear action encoding with simple explanatory frame axioms, a linear action encoding with complex explanatory frame axioms, and a parallel action encoding. Results on a suite of test problems indicate that linear action encodings with simple explanatory frame axioms and parallel action encodings show particular promise, improving ZANDER’s efficiency by as much as three orders of magnitude.