At the thirty-sixth AAAI Conference on Artificial Intelligence, our AI planning researcher Daniel Fišer received the Honorable Mention Oustanding Paper Award for his paper Operator-Potential Heuristics for Symbolic Search. The co-authors were Álvaro Torralba from the Aalborg University, Denmark and Jörg Hoffmann from the Saarland University, Germany. Their paper was warded in the most prestigious category where papers that exemplify the highest standards in technical contribution and exposition compete. Congratulations on this significant success!
Symbolic search, using Binary Decision Diagrams (BDDs) to represent sets of states, is a competitive approach to 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 a time. Indeed, progress using heuristic functions in symbolic search has been limited and even very informed heuristics have been shown to be detrimental. Here we show how this connection can be made stronger for LP-based potential heuristics. Our key observation is that, for this family of heuristic functions, the change of heuristic value induced by each operator can be precomputed. This facilitates their smooth integration into symbolic search. Our experiments show that this can pay off significantly: we establish a new state-of-the-art in optimal symbolic planning. Download the paper here.