A Hybrid Abductive Inductive Proof Procedure
A Hybrid Abductive Inductive Proof Procedure
复制标题
混合溯因归纳证明程序
作者：
O. Ray;K. Broda;A. Russo
本文介绍了一个证明过程，集成了溯因逻辑编程（ALP）和归纳逻辑编程（ILP）自动学习的一阶霍恩子句理论的例子和背景知识。这项工作建立在最近的一种称为混合外展诱导学习（HAIL）的方法的基础上，展示了语言偏见如何实际有效地融入学习过程。一个证明程序冰雹提出了利用一组用户指定的模式声明学习假设，满足给定的语言偏见。一个语义，准确地描述了预期的假设空间，并包括推导的证明过程中的假设。描述了一种实施方式，其结合了在ILP程序内的Kakas-Mancarella ALP程序的扩展，该ILP程序概括了Muggleton的Progol系统。明确整合的溯因和归纳，允许推导多个子句假设响应于一个单一的种子的例子，并使推理的缺失类型的信息，以前不可能的方式。
This paper introduces a proof procedure that integrates Abductive Logic Programming (ALP) and Inductive Logic Programming (ILP) to automate the learning of first order Horn clause theories from examples and background knowledge. The work builds upon a recent approach called Hybrid Abductive Inductive Learning (HAIL) by showing how language bias can be practically and usefully incorporated into the learning process. A proof procedure for HAIL is proposed that utilises a set of user specified mode declarations to learn hypotheses that satisfy a given language bias. A semantics is presented that accurately characterises the intended hypothesis space and includes the hypotheses derivable by the proof procedure. An implementation is described that combines an extension of the Kakas-Mancarella ALP procedure within an ILP procedure that generalises the Progol system of Muggleton. The explicit integration of abduction and induction is shown to allow the derivation of multiple clause hypotheses in response to a single seed example and to enable the inference of missing type information in a way not previously possible.