Towards semi-automatic learning-based model transformation

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Recently, [16] showed that the nogoods inferred by learning solvers can be used to improve a problem model, by detecting constraints that can be strengthened and new redundant constraints. However, the detection process was manual and required in-depth knowledge of both the learning solver and the model transformations performed by the compiler. In this paper we provide the first steps towards a (largely) automatic detection process. In particular, we discuss how nogoods can be automatically simplified, connected back to the constraints in the model, and grouped into more general “patterns” for which common facts might be found. These patterns are easier to understand and provide stronger evidence of the importance of particular constraints. We also show how nogoods generated by different search strategies and problem instances can increase our confidence in the usefulness of these patterns. Finally, we identify significant challenges and avenues for future research.

Original languageEnglish
Title of host publicationPrinciples and Practice of Constraint Programming
Subtitle of host publication24th International Conference, CP 2018 Lille, France, August 27–31, 2018 Proceedings
EditorsJohn Hooker
Place of PublicationCham Switzerland
Number of pages17
ISBN (Electronic)9783319983349
ISBN (Print)9783319983332
Publication statusPublished - 1 Jan 2018
EventInternational Conference on Principles and Practice of Constraint Programming 2018 - Lille, France
Duration: 27 Aug 201831 Aug 2018
Conference number: 24th

Publication series

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


ConferenceInternational Conference on Principles and Practice of Constraint Programming 2018
Abbreviated titleCP 2018
Internet address

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