Work

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

Year: 2025

Type: conference-paper

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.

Source: Proceedings of the AAAI Conference on Artificial Intelligence

DOI: 10.1609/aaai.v39i14.33634

Authors: Jorge Fandinno, Lute Lillo

Institutions: University of Nebraska at Omaha, University of Vermont

Language: English

Topic: Logic, Reasoning, and Knowledge

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: National Science Foundation

Awards: CAREER: Answer Set Programming for Quantitative Information, G6671297155