DiverSAT: A Novel and Effective Local Search Algorithm for Diverse SAT Problem

Authors

  • Jiaxin Liang School of Information Science and Technology, Northeast Normal University, China
  • Junping Zhou School of Information Science and Technology, Northeast Normal University, China
  • Minghao Yin School of Information Science and Technology, Northeast Normal University, China Key Laboratory of Applied Statistics of MOE, Northeast Normal University, China

DOI:

https://doi.org/10.1609/aaai.v39i11.33228

Abstract

For many real-world problems, users are often interested not only in finding a single solution but in obtaining a sufficiently diverse collection of solutions. In this work, we consider the Diverse SAT problem, aiming to find a set of diverse satisfying assignments for a given propositional formula. We propose a novel and effective local search algorithm, DiverSAT, to solve the problem. To cope with diversity, we introduce three heuristics and a perturbation strategy based on some relevant information. We conduct extensive experiments on a large number of public benchmarks, collected from semiformal hardware verification, logistics planning, and other domains. The results show that DiverSAT outperforms the existing algorithms on most of these benchmarks.

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Published

2025-04-11

How to Cite

Liang, J., Zhou, J., & Yin, M. (2025). DiverSAT: A Novel and Effective Local Search Algorithm for Diverse SAT Problem. Proceedings of the AAAI Conference on Artificial Intelligence, 39(11), 11290–11298. https://doi.org/10.1609/aaai.v39i11.33228

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Section

AAAI Technical Track on Constraint Satisfaction and Optimization