用于归纳逻辑编程的高效命题系统

IF 10.7 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Artificial Intelligence Review Pub Date : 2024-10-17 DOI:10.1007/s10462-024-10928-7
Marco Gavanelli, Pascual Julián-Iranzo, Fernando Sáenz-Pérez
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引用次数: 0

摘要

归纳逻辑编程(ALP)是通过abducibles对逻辑编程进行假设推理的扩展,这种扩展能够用形式化方法处理诊断、规划和验证等有趣的问题。这一扩展的实现一直使用 Prolog 元解释器和带有约束处理规则(CHR)的 Prolog 程序。虽然后者为主机系统增加了一个简洁高效的接口,但对于大型程序而言,其性能仍然受到影响。在此,我们关注的是如何通过编译方法获得性能更高的 SCIFF 系统实现。作为实现这一长期目标的第一步,本文提出了一种遵循 SCIFF 的命题式 ALP 系统,无需使用 CHR,并实现了更好的性能。
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An efficient propositional system for Abductive Logic Programming

Abductive logic programming (ALP) extends logic programming with hypothetical reasoning by means of abducibles, an extension able to handle interesting problems, such as diagnosis, planning, and verification with formal methods. Implementations of this extension have been using Prolog meta-interpreters and Prolog programs with Constraint Handling Rules (CHR). While the latter adds a clean and efficient interface to the host system, it still suffers in performance for large programs. Here, the concern is to obtain a more performant implementation of the SCIFF system following a compiled approach. This paper, as a first step in this long term goal, sets out a propositional ALP system following SCIFF, eliminating the need for CHR and achieving better performance.

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来源期刊
Artificial Intelligence Review
Artificial Intelligence Review 工程技术-计算机:人工智能
CiteScore
22.00
自引率
3.30%
发文量
194
审稿时长
5.3 months
期刊介绍: Artificial Intelligence Review, a fully open access journal, publishes cutting-edge research in artificial intelligence and cognitive science. It features critical evaluations of applications, techniques, and algorithms, providing a platform for both researchers and application developers. The journal includes refereed survey and tutorial articles, along with reviews and commentary on significant developments in the field.
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