Constrained Neural Networks for Interpretable Heuristic Creation to Optimise Computer Algebra Systems

Matthew England, Dorian Florescu

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

We present a new methodology for utilising machine learning technology in symbolic computation research. We explain how a well known human-designed heuristic to make the choice of variable ordering in cylindrical algebraic decomposition may be represented as a constrained neural network. This allows us to then use machine learning methods to further optimise the heuristic, leading to new networks of similar size, representing new heuristics of similar complexity as the original human-designed one. We present this as a form of ante-hoc explainability for use in computer algebra development.
Original languageEnglish
Title of host publicationProceedings of the 9th International Congress on Mathematical Software
PublisherSpringer
Number of pages10
Publication statusPublished - 22 Apr 2024

Publication series

NameLecture Notes in Computer Science

Keywords

  • Computer Algebra
  • Cylindrical Algebraic Decomposition
  • Machine learning
  • Explainable AI
  • interpretability
  • XAI

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