Optimizing general chain programs

Anke D. Rieger
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引用次数: 1

Abstract

The goal of knowledge compilation is to transform programs in order to speed up their evaluation. In Inductive Logic Programming, two major approaches to speed-up learning exist: Approaches that intertwine the learning and the optimization process and approaches that separate these two processes. We follow the latter approach and present a new equivalence-preserving transformation method for programs with ordered clauses. It eliminates redundancies that make forward inference procedures slow. We introduce general chain rules, a specific class of ordered clauses, whose syntactical features are exploited in a new forward inference method. The comparison of the time needed by this method to evaluate the transformed program with the time needed by a standard forward inference procedure for the original program confirms the expected speed-up.

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优化通用链程序
知识编译的目的是对程序进行改造,加快程序的评估速度。在归纳逻辑编程中,存在两种主要的加速学习方法:将学习和优化过程交织在一起的方法和将这两个过程分开的方法。我们采用后一种方法,给出了一种新的保等价变换方法。它消除了使前向推理过程变慢的冗余。我们引入了一般链式规则,这是一类特殊的有序子句,它的语法特征被利用在一种新的前向推理方法中。将该方法对转换后的程序进行评估所需的时间与对原始程序进行标准前向推理所需的时间进行比较,证实了预期的加速。
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