An overview of machine learning techniques in constraint solving

Andrei Popescu*, Seda Polat Erdeniz, Alexander Felfernig, Mathias Uta, Müslüm Atas, Viet Man Le, Klaus Pilsl, Martin Enzelsberger, Trang Tran

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Constraint solving is applied in different application contexts. Examples thereof are the configuration of complex products and services, the determination of production schedules, and the determination of recommendations in online sales scenarios. Constraint solvers apply, for example, search heuristics to assure adequate runtime performance and prediction quality. Several approaches have already been developed showing that machine learning (ML) can be used to optimize search processes in constraint solving. In this article, we provide an overview of the state of the art in applying ML approaches to constraint solving problems including constraint satisfaction, SAT solving, answer set programming (ASP) and applications thereof such as configuration, constraint-based recommendation, and model-based diagnosis. We compare and discuss the advantages and disadvantages of these approaches and point out relevant directions for future work.
Original languageEnglish
Pages (from-to)91-118
Number of pages28
JournalJournal of Intelligent Information Systems
Issue number1
Publication statusPublished - Feb 2022


  • Answer set programming
  • Applications
  • Boolean satisfiability
  • Constraint satisfaction
  • Constraint solving
  • Machine learning

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Information Systems
  • Hardware and Architecture
  • Computer Networks and Communications

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