A Machine Learning System to Improve the Performance of ASP Solving Based on Encoding Selection

Liu Liu, Mirek Truszczynski, Yuliya Lierler

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Answer set programming (ASP) has long been used for modeling and solving hard search problems. Experience shows that the performance of ASP tools on different ASP encodings of the same problem may vary greatly from instance to instance and it is rarely the case that one encoding outperforms all others. We describe a system and its implementation that given a set of encodings and a training set of instances, builds performance models for the encodings, predicts the execution time of these encodings on new instances, and uses these predictions to select an encoding for solving.

Original languageEnglish
Title of host publicationLogic Programming and Nonmonotonic Reasoning - 16th International Conference, LPNMR 2022, Proceedings
EditorsGeorg Gottlob, Daniela Inclezan, Marco Maratea
Pages415-428
Number of pages14
DOIs
StatePublished - 2022
Event16th International Conference on Logic Programming and Nonmonotonic Reasoning, LPNMR 2022 - Genoa, Italy
Duration: Sep 5 2022Sep 9 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13416 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Conference on Logic Programming and Nonmonotonic Reasoning, LPNMR 2022
Country/TerritoryItaly
CityGenoa
Period9/5/229/9/22

Bibliographical note

Funding Information:
The authors acknowledge the support of the NSF grant IIS 1707371.

Funding Information:
support of the NSF grant IIS

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Keywords

  • answer set programming
  • encoding selection
  • machine learning

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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