限定逻辑规划中基于相似性的推理

R. Caballero, M. Rodríguez-Artalejo, Carlos A. Romero-Díaz
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引用次数: 23

摘要

基于相似度的逻辑规划(similarity based Logic Programming,简称SLP)是一种支持灵活信息检索应用的近似推理方法。这种方法使用了程序签名中符号之间的模糊相似关系,同时保留了经典LP中程序子句的语法。另一个最近的提议是QLP(D)方案的合格逻辑规划,一个扩展的LP范式,支持近似推理和更多。这种方法使用带注释的程序子句和参数化给定的域D,域D的元素通过测量它们与各种用户期望的接近程度来限定逻辑断言。在本文中,我们提出了一个更具表达性的方案SQLP(l, D),它将SLP和QLP(D)作为特殊情况纳入其中。我们还证明了SQLP(l, D)规划可以转化为语义等价的QLP(D)规划。因此,现有的QLP(D)实现可用于为基于相似性的推理提供有效的支持
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Similarity-based reasoning in qualified logic programming
Similarity-based Logic Programming (briefly, SLP) has been proposed to enhance the LP paradigm with a kind of approximate reasoning which supports flexible information retrieval applications. This approach uses a fuzzy similarity relation ℝ between symbols in the program's signature, while keeping the syntax for program clauses as in classical LP. Another recent proposal is the QLP(D) scheme for Qualified Logic Programming, an extension of the LP paradigm which supports approximate reasoning and more. This approach uses annotated program clauses and a parametrically given domain D whose elements qualify logical assertions by measuring their closeness to various users' expectations. In this paper we propose a more expressive scheme SQLP(ℝ, D) which subsumes both SLP and QLP(D) as particular cases. We also show that SQLP(ℝ, D) programs can be transformed into semantically equivalent QLP(D) programs. As a consequence, existing QLP(D) implementations can be used to give efficient support for similarity-based reasoning
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