Symbolic Pattern Databases in Heuristic Search Planning

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

This paper invents symbolic pattern databases (SPDB) to combine two influencing aspects for recent progress in domain-independent action planning, namely heuristic search and model checking. SPDBs are off-line computed dictionaries, generated in symbolic backward traversals of automatically inferred planning space abstractions. The entries of SPDBs serve as heuristic estimates to accelerate explicit and symbolic, approximate and optimal heuristic search planners. Selected experiments highlight that the symbolic representation yields much larger and more accurate pattern databases than the ones generated with explicit methods.