Enhance Diversified Top-k MaxSAT Solving by Incorporating New Strategy for Generating Diversified Initial Assignments (Student Abstract)

Authors

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

DOI:

https://doi.org/10.1609/aaai.v38i21.30473

Keywords:

Diversified Top-k MaxSAT, Local Search, Top-k

Abstract

The Diversified Top-k MaxSAT (DTKMS) problem is an extension of MaxSAT. The objective of DTKMS is to find k feasible assignments of a given formula, such that each assignment satisfies all hard clauses and the k assignments together satisfy the maximum number of soft clauses. This paper presents a local search algorithm, DTKMS-DIA, which incorporates a new approach to generating initial assignments. Experimental results indicate that DTKMS-DIA can achieve attractive performance on 826 instances compared with state-of-the-art solvers.
AAAI-24 / IAAI-24 / EAAI-24 Proceedings Cover

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Published

2024-03-24

How to Cite

Liang, J., Zhou, J., & Yin, M. (2024). Enhance Diversified Top-k MaxSAT Solving by Incorporating New Strategy for Generating Diversified Initial Assignments (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence, 38(21), 23561-23562. https://doi.org/10.1609/aaai.v38i21.30473

Issue

Vol. 38 No. 21: IAAI-24, EAAI-24, AAAI-24 Student Abstracts, Undergraduate Consortium and Demonstrations

Section

AAAI Student Abstract and Poster Program