ASP(Q)问题的高效求解器

IF 1.4 2区 数学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING Theory and Practice of Logic Programming Pub Date : 2023-07-01 DOI:10.1017/s1471068423000121
WOLFGANG FABER, GIUSEPPE MAZZOTTA, FRANCESCO RICCA
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引用次数: 0

摘要

ASP(Q)扩展了答案集编程(ASP),允许从整个多项式层次结构对问题进行声明性和模块化建模。ASP(Q)的第一个实现,称为QASP,是基于对量化布尔公式(QBF)的翻译,目的是利用发达和成熟的QBF求解技术。然而,qasp中使用的QBF编码的实现是非常通用的,并且由于大量的符号和子句,可能会产生难以对现有QBF求解器进行评估的公式。在本文中,我们提出了一种基于QASP思想的新实现,它具有更高效的编码过程和QBF中ASP(Q)程序的新优化编码。新的编码产生更小的公式(就量词、变量和子句的数量而言),并产生更有效的评估过程。算法选择策略自动组合多个求解qbf的后端以进一步提高性能。在已知基准上进行的实验分析表明,新系统优于QASP。
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An Efficient Solver for ASP(Q)
Abstract Answer Set Programming with Quantifiers ASP(Q) extends Answer Set Programming (ASP) to allow for declarative and modular modeling of problems from the entire polynomial hierarchy. The first implementation of ASP(Q), called QASP, was based on a translation to Quantified Boolean Formulae (QBF) with the aim of exploiting the well-developed and mature QBF-solving technology. However, the implementation of the QBF encoding employed in qasp is very general and might produce formulas that are hard to evaluate for existing QBF solvers because of the large number of symbols and subclauses. In this paper, we present a new implementation that builds on the ideas of QASP and features both a more efficient encoding procedure and new optimized encodings of ASP(Q) programs in QBF. The new encodings produce smaller formulas (in terms of the number of quantifiers, variables, and clauses) and result in a more efficient evaluation process. An algorithm selection strategy automatically combines several QBF-solving back-ends to further increase performance. An experimental analysis, conducted on known benchmarks, shows that the new system outperforms QASP.
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来源期刊
Theory and Practice of Logic Programming
Theory and Practice of Logic Programming 工程技术-计算机:理论方法
CiteScore
4.50
自引率
21.40%
发文量
40
审稿时长
>12 weeks
期刊介绍: Theory and Practice of Logic Programming emphasises both the theory and practice of logic programming. Logic programming applies to all areas of artificial intelligence and computer science and is fundamental to them. Among the topics covered are AI applications that use logic programming, logic programming methodologies, specification, analysis and verification of systems, inductive logic programming, multi-relational data mining, natural language processing, knowledge representation, non-monotonic reasoning, semantic web reasoning, databases, implementations and architectures and constraint logic programming.
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