不带阻塞子句的SAT和SMT的不相交投影枚举

IF 4.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Giuseppe Spallitta , Roberto Sebastiani , Armin Biere
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引用次数: 0

摘要

近年来,全解可满足性(AllSAT)及其扩展全解可满足模理论(AllSMT)在形式验证和人工智能应用中变得越来越重要。这些问题的目标是枚举一个公式的所有令人满意的赋值(分别针对SAT和SMT问题),使它们对测试生成、模型检查和概率推断有用。然而,由于搜索空间的指数增长和阻塞子句导致的低效率,传统的AllSAT算法面临着巨大的计算挑战,阻塞子句会导致内存爆炸并降低长期的单元传播性能。本文提出了两个新的求解器:TabularAllSAT和TabularAllSMT。这两种解决方案都将冲突驱动子句学习(CDCL)与时间回溯相结合,以提高效率,同时确保不连接枚举。为了检索紧凑的部分分配,我们提出了一种新的兼容时间回溯的主动隐含收缩算法,以最小化部分分配的数量,降低整体搜索复杂度。此外,我们扩展了求解器框架,以有效地处理投影枚举和SMT公式,并采用基线框架来整合理论推理以及重要变量和非重要变量的区分。广泛的实验评估表明,与最先进的求解器相比,我们的方法具有优势,特别是在需要投影和基于smt的推理的场景中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Disjoint projected enumeration for SAT and SMT without blocking clauses
All-Solution Satisfiability (AllSAT) and its extension, All-Solution Satisfiability Modulo Theories (AllSMT), have become more relevant in recent years, mainly in formal verification and artificial intelligence applications. The goal of these problems is the enumeration of all satisfying assignments of a formula (for SAT and SMT problems, respectively), making them useful for test generation, model checking, and probabilistic inference. Nevertheless, traditional AllSAT algorithms face significant computational challenges due to the exponential growth of the search space and inefficiencies caused by blocking clauses, which cause memory blowups and degrade unit propagation performance in the long term. This paper presents two novel solvers: TabularAllSAT, a projected AllSAT solver, and TabularAllSMT, a projected AllSMT solver. Both solvers combine Conflict-Driven Clause Learning (CDCL) with chronological backtracking to improve efficiency while ensuring disjoint enumeration. To retrieve compact partial assignments we propose a novel aggressive implicant shrinking algorithm, compatible with chronological backtracking, to minimize the number of partial assignments, reducing overall search complexity. Furthermore, we extend the solver framework to handle projected enumeration and SMT formulas effectively and efficiently, adapting the baseline framework to integrate theory reasoning and the distinction between important and non-important variables. An extensive experimental evaluation demonstrates the superiority of our approach compared to state-of-the-art solvers, particularly in scenarios requiring projection and SMT-based reasoning.
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来源期刊
Artificial Intelligence
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.
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