Theoretical and experimental results for planning with learned binarized neural network transition models

Buser Say, Jo Devriendt, Jakob Nordström, Peter J. Stuckey

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3 Citations (Scopus)


We study planning problems where the transition function is described by a learned binarized neural network (BNN). Theoretically, we show that feasible planning with a learned BNN model is NP-complete, and present two new constraint programming models of this task as a mathematical optimization problem. Experimentally, we run solvers for constraint programming, weighted partial maximum satisfiability, 0–1 integer programming, and pseudo-Boolean optimization, and observe that the pseudo-Boolean solver outperforms previous approaches by one to two orders of magnitude. We also investigate symmetry handling for planning problems with learned BNNs over long horizons. While the results here are less clear-cut, we see that exploiting symmetries can sometimes reduce the running time of the pseudo-Boolean solver by up to three orders of magnitude.

Original languageEnglish
Title of host publicationPrinciples and Practice of Constraint Programming
Subtitle of host publication26th International Conference, CP 2020 Louvain-la-Neuve, Belgium, September 7–11, 2020 Proceedings
EditorsHelmut Simonis
Place of PublicationCham Switzerland
Number of pages18
ISBN (Electronic)9783030584757
ISBN (Print)9783030584740
Publication statusPublished - 2020
EventInternational Conference on Principles and Practice of Constraint Programming 2020 - Louvain-la-Neuve, Belgium
Duration: 7 Sep 202011 Sep 2020
Conference number: 26th (Proceedings) (Website)

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Principles and Practice of Constraint Programming 2020
Abbreviated titleCP2020
Internet address


  • Automated planning
  • Binarized neural networks
  • Cutting planes reasoning
  • Mathematical optimization
  • Pseudo-Boolean optimization
  • Symmetry

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