Checking Consistency of CP-Theory Preferences in Polynomial Time

We investigate the problem of checking the consistency of qualitative preferences expressed in CP-theory. This problem is PSPACE-Complete even when the preferences are locally consistent or the preference variables have binary domain. We present a new sufficient condition for consistency of preferences and show that the condition can be checked in polynomial time in settings of practical relevance (locally consistent or binary domain preference variables). We further show how the resulting sufficient condition can be used to efficiently identify a subset of outcomes that are non-dominated with respect to a set of qualitative preferences.

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Publication Details

Journal
Proceedings of the AAAI Conference on Artificial Intelligence
Published
2025-04-11
DOI
https://doi.org/10.1609/aaai.v39i14.33659
Primary Topic
Formal Methods in Verification
Type
article
Field-Weighted Citation Impact
0.00

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article

Checking Consistency of CP-Theory Preferences in Polynomial Time

Vasant Honavar, Samik Basu, Erik Rauer
Proceedings of the AAAI Conference on Artificial Intelligence
Formal Methods in Verification
article

Checking Consistency of CP-Theory Preferences in Polynomial Time

Vasant Honavar, Samik Basu, Erik Rauer
article en

Abstract

We investigate the problem of checking the consistency of qualitative preferences expressed in CP-theory. This problem is PSPACE-Complete even when the preferences are locally consistent or the preference variables have binary domain. We present a new sufficient condition for consistency of preferences and show that the condition can be checked in polynomial time in settings of practical relevance (locally consistent or binary domain preference variables). We further show how the resulting sufficient condition can be used to efficiently identify a subset of outcomes that are non-dominated with respect to a set of qualitative preferences.

Proceedings of the AAAI Conference on Artificial IntelligenceVol. 39(14)
Iowa State University (US), Artificial Intelligence in Medicine (Canada) (CA)
National Science Foundation
Openalex Percentile: Top 89%
Formal Methods in Verification
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