Operator-Potential Heuristics for Symbolic Search

Symbolic search, using Binary Decision Diagrams (BDDs) to represent sets of states, is a competitive approach optimal planning. Yet heuristic search in this context remains challenging. The many advances on admissible planning heuristics are not directly applicable, as they evaluate one state at time. Indeed, progress functions symbolic has been limited and even very informed have shown be detrimental. Here we show how connection can made stronger for LP-based potential heuristics. Our key observation that, family functions, the change value induced by each operator precomputed. This facilitates their smooth integration into search. experiments that pay off significantly: establish new art