Planning with Multiple Action-Cost Estimates

Eyal Weiss, Gal A. Kaminka

Research output: Contribution to journalConference articlepeer-review

2 Scopus citations


AI Planning require computing the costs of ground actions. While often assumed to be negligible, the run-time of this computation can become a major component in the overall planning run-time. To address this, we introduce planning with multiple action cost estimates, a generalization of classical planning, where action cost can be incrementally determined using multiple estimation procedures, which trade computational effort for increasingly tightening bounds on the exact cost. We then present ACE, a generalized A*, to solve such problems. We provide theoretical guarantees, and extensive experiments that show considerable run-time savings compared to alternatives.

Original languageEnglish
Pages (from-to)427-437
Number of pages11
JournalProceedings International Conference on Automated Planning and Scheduling, ICAPS
Issue number1
StatePublished - 2023
Event33rd International Conference on Automated Planning and Scheduling, ICAPS 2023 - Prague, Czech Republic
Duration: 8 Jul 202313 Jul 2023

Bibliographical note

Publisher Copyright:
Copyright © 2023, Association for the Advancement of Artificial Intelligence ( All rights reserved.


The authors thank Alexander Shleyfman for his constructive feedback. The research was partially funded by ISF Grant #2306/18 and BSF-NSF grant 2017764. Thanks to K. Ushi. Eyal Weiss is supported by the Adams Fellowship Program of the Israel Academy of Sciences and Humanities and by Bar-Ilan University’s President Scholarship.

FundersFunder number
Bar-Ilan University
Israel Academy of Sciences and Humanities
Israel Science Foundation2306/18


    Dive into the research topics of 'Planning with Multiple Action-Cost Estimates'. Together they form a unique fingerprint.

    Cite this