SC-Square: Future Progress with Machine Learning

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Abstract

The algorithms employed by our communities are often underspecified, and thus have multiple implementation choices, which do not effect the correctness of the output, but do impact the efficiency or even tractability of its production.
In this extended abstract, to accompany a keynote talk at the 2021 SC-Square Workshop, we survey recent work (both the author's and from the literature) on the use of Machine Learning technology to improve algorithms of interest to SC-Square.
Original languageEnglish
Title of host publicationSC-Square 2021 Proceedings
EditorsCurtis Bright, James Davenport
PublisherCEUR Workshop Proceedings
Pages7-16
Number of pages10
Volume3273
Publication statusPublished - 14 Nov 2022
Event6th International Workshop on Satisfiability Checking and Symbolic Computation: Bridging Two Communities to Solve Real Problems - College Station, United States
Duration: 19 Aug 202120 Aug 2021
Conference number: 6
http://www.sc-square.org/CSA/workshop6.html

Publication series

NameCEUR Workshop Proceedings
ISSN (Print)1613-0073

Conference

Conference6th International Workshop on Satisfiability Checking and Symbolic Computation
Abbreviated titleSC2 Workshop 2021
Country/TerritoryUnited States
CityCollege Station
Period19/08/2120/08/21
Internet address

Bibliographical note

© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

Keywords

  • machine learning
  • symbolic computation
  • computer algebra systems
  • satisfiability checking
  • SMT solvers

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