Operator-Potential Heuristics for Symbolic Search

Daniel Fišer, Alvaro Torralba, Jörg Hoffmann

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Abstrakt

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.
OriginalsprogEngelsk
TitelProceedings of the AAAI Conference on Artificial Intelligence, 36
Vol/bind36
ForlagAAAI Press
Publikationsdatojun. 2022
Sider 9750-9757
StatusUdgivet - jun. 2022
Begivenhed36th AAAI Conference on Artificial Intelligence 2022 -
Varighed: 22 feb. 20221 mar. 2022
https://aaai-2022.virtualchair.net/index.html

Konference

Konference36th AAAI Conference on Artificial Intelligence 2022
Periode22/02/202201/03/2022
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