Operator-Potential Heuristics for Symbolic Search


  • Daniel Fišer Saarland University, Saarland Informatics Campus, Saarbrücken, Germany Czech Technical University in Prague, Faculty of Electrical Engineering, Czech Republic
  • Álvaro Torralba Aalborg University, Denmark
  • Jörg Hoffmann Saarland University, Saarland Informatics Campus, Saarbrücken, Germany




Planning, Routing, And Scheduling (PRS)


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.




How to Cite

Fišer, D., Torralba, Álvaro, & Hoffmann, J. (2022). Operator-Potential Heuristics for Symbolic Search. Proceedings of the AAAI Conference on Artificial Intelligence, 36(9), 9750-9757. https://doi.org/10.1609/aaai.v36i9.21210



AAAI Technical Track on Planning, Routing, and Scheduling