Symbolic A* Search with Pattern Databases and the Merge-and-Shrink Abstraction

摘要

The efficiency of heuristic search planning crucially depends on the quality of the search heuristic, while succinct representations of state sets in decision diagrams can save large amounts of memory in the exploration. BDDA* - a symbolic version of A* search - combines the two approaches into one algorithm. This paper compares two of the leading heuristics for sequential-optimal planning: the merge-and-shrink and the pattern databases heuristic, both of which can be compiled into a vector of BDDs and be used in BDDA*. The impact of optimizing the variable ordering is highlighted and experiments on benchmark domains are reported.