带有立方体学习功能的 QCDCL 或纯粹的字面排除 - 哪种方法最好？

IF 5.1 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Artificial Intelligence Pub Date : 2024-08-08 DOI:10.1016/j.artint.2024.104194

Benjamin Böhm , Tomáš Peitl , Olaf Beyersdorff

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引用次数: 0

摘要

量化冲突驱动子句学习（QCDCL）是求解量化布尔公式（QBF）的主要方法之一。我们对 QCDCL 的几个版本进行了形式化和研究，其中包括立方学习和/或纯字面消除，并通过证明复杂性技术对由此产生的求解变体进行了形式化比较。我们的结果表明，几乎所有的 QCDCL 变体在证明大小（以及求解器运行时间）方面都是指数级的，这表明了如何实际实现 QCDCL 的不同正交方法。

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QCDCL with cube learning or pure literal elimination – What is best?

Quantified conflict-driven clause learning (QCDCL) is one of the main approaches for solving quantified Boolean formulas (QBF). We formalise and investigate several versions of QCDCL that include cube learning and/or pure-literal elimination, and formally compare the resulting solving variants via proof complexity techniques. Our results show that almost all of the QCDCL variants are exponentially incomparable with respect to proof size (and hence solver running time), pointing towards different orthogonal ways how to practically implement QCDCL.

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来源期刊

Artificial Intelligence 工程技术-计算机：人工智能

CiteScore

11.20

自引率

1.40%

发文量

118

审稿时长

8 months

期刊介绍： The Journal of Artificial Intelligence (AIJ) welcomes papers covering a broad spectrum of AI topics, including cognition, automated reasoning, computer vision, machine learning, and more. Papers should demonstrate advancements in AI and propose innovative approaches to AI problems. Additionally, the journal accepts papers describing AI applications, focusing on how new methods enhance performance rather than reiterating conventional approaches. In addition to regular papers, AIJ also accepts Research Notes, Research Field Reviews, Position Papers, Book Reviews, and summary papers on AI challenges and competitions.