Using Symmetries to Lift Satisfiability Checking

Pierre Carbonnelle, Gottfried Schenner, Maurice Bruynooghe, Bart Bogaerts, Marc Denecker

Research output: Chapter in Book/Report/Conference proceedingConference paper

Abstract

We analyze how symmetries can be used to compress structures (also known as interpretations) onto a smaller domain without loss of information. This analysis suggests the possibility to solve satisfiability problems in the compressed domain for better performance. Thus, we propose a 2-step novel method: (i) the sentence to be satisfied is automatically translated into an equisatisfiable sentence over a “lifted” vocabulary that allows domain compression; (ii) satisfiability of the lifted sentence is checked by growing the (initially unknown) compressed domain until a satisfying structure is found. The key issue is to ensure that this satisfying structure can always be expanded into an uncompressed structure that satisfies the original sentence to be satisfied. We present an adequate translation for sentences in typed first-order logic extended with aggregates. Our experimental evaluation shows large speedups for generative configuration problems. The method also has applications in the verification of software operating on complex data structures. Our results justify further research in automatic translation of sentences for symmetry reduction.

Original languageEnglish
Title of host publicationThirty-Eighth {AAAI} Conference on Artificial Intelligence, AAAI 2024
EditorsMichael Woolridge, Jennifer Dy, Sriraam Natarajan
PublisherAAAI Press
Pages7961-7968
Number of pages8
Volume38
Edition8
DOIs
Publication statusPublished - 25 Mar 2024
EventThirty-Eighth {AAAI} Conference on Artificial Intelligence, {AAAI}
2024
- Vancouver, Vancouver, Canada
Duration: 20 Feb 202427 Feb 2024
Conference number: 38

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
ISSN (Print)2159-5399

Conference

ConferenceThirty-Eighth {AAAI} Conference on Artificial Intelligence, {AAAI}
2024
Abbreviated titleAAAI
Country/TerritoryCanada
CityVancouver
Period20/02/2427/02/24

Bibliographical note

Funding Information:
This research received funding from the Flemish Government under the \u201COnderzoeksprogramma Artifici\u00EBle Intelligentie (AI) Vlaanderen\u201D programme.

Publisher Copyright:
Copyright © 2024, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

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