Exploiting Large Language Models for the Automated Generation of Constraint Satisfaction Problems

Lothar Hotz*, Christian Bähnisch, Sebastian Lubos, Alexander Felfernig, Albert Haag, Johannes Twiefel

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract

Constraint Satisfaction Problems (CSPs) are a core technology that solves many real-world problems, especially for configuration tasks. A key success factor in this context is an efficient knowledge acquisition process where domain experts and knowledge engineers (developers of CSPs) should develop an agreement on the correctness of the expanding knowledge base as soon as possible. In this paper, we show how large language models (LLMs) can be applied to the automated generation of solutions for constraint satisfaction problems thus reducing overheads related to CSP development and maintenance in the future.

Original languageEnglish
Pages (from-to)91-100
Number of pages10
JournalCEUR Workshop Proceedings
Volume3812
Publication statusPublished - 2024
Event26th International Workshop on Configuration, ConfWS 2024 - Girona, Spain
Duration: 2 Sept 20243 Sept 2024

Keywords

  • Automated Generation
  • Constraint Satisfaction Problems
  • Knowledge Acquisition
  • Large Language Models

ASJC Scopus subject areas

  • General Computer Science

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