Solving Epistemic Logic Programs Using Generate-and-Test with Propagation

This paper introduces a general framework for generate-and-test-based solvers for epistemic logic programs that can be instantiated with different generate and test programs, and it provides sufficient conditions on those programs for the correctness of the solvers built using this framework. It also introduces a new generator program that incorporates the propagation of epistemic consequences and shows that this can exponentially reduce the number of candidates that need to be tested while only incurring a linear overhead. We implement a new solver based on these theoretical findings and experimentally show that it outperforms existing solvers by achieving a ~3.3x speed-up and solving 87% more instances on well-known benchmarks.

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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.v39i14.33634
Primary Topic
Software Testing and Debugging Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Solving Epistemic Logic Programs Using Generate-and-Test with Propagation

Jorge Fandinno, Lute Lillo
Proceedings of the AAAI Conference on Artificial Intelligence
Software Testing and Debugging Techniques
article

Solving Epistemic Logic Programs Using Generate-and-Test with Propagation

Jorge Fandinno, Lute Lillo
article en

Abstract

This paper introduces a general framework for generate-and-test-based solvers for epistemic logic programs that can be instantiated with different generate and test programs, and it provides sufficient conditions on those programs for the correctness of the solvers built using this framework. It also introduces a new generator program that incorporates the propagation of epistemic consequences and shows that this can exponentially reduce the number of candidates that need to be tested while only incurring a linear overhead. We implement a new solver based on these theoretical findings and experimentally show that it outperforms existing solvers by achieving a ~3.3x speed-up and solving 87% more instances on well-known benchmarks.

Proceedings of the AAAI Conference on Artificial IntelligenceVol. 39(14)
University of Nebraska at Omaha (US)
National Science Foundation
Openalex Percentile: Top 77%
Software Testing and Debugging Techniques
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