例外を有する一般規則を学習する帰納推論システムの研究

在归纳逻辑程序设计(ILP)的大部分工作中，确定的Horn程序或经典的子句程序都是以学习逻辑程序的形式考虑的。然而，人工智能中关于知识表示的研究，特别是在非单调推理方面的工作表明，这种单调程序不足以表示我们的常识知识。要学习分类层次结构中的默认规则或概念，需要一种处理非单调推理的学习机制。提出了一个学习扩展逻辑程序(ELP)的学习系统LELP。ELP允许两种否定，并且可以代表不完整的知识。LELP可以在给定不完整的正反范例和背景知识的情况下，以ELP的形式学习带有例外的缺省规则。在LELP中，还可以通过递归调用异常识别算法来学习分层默认设置。此外，当一些实例可能被分类为正向和负向时，也可以学习不确定规则。本文的主要工作如下：1.证明了LELP的正确性，并对其性质进行了分析。2.采用自顶向下和自底向上两种生成通用规则的算法，实现了不同版本的LELP。为了提高系统的速度，我们用Java重新实现了LELP，并将遗传算法应用到LELP中，自动生成搜索偏差。3.我们考虑了LELP的一个扩展，它不仅可以学习ELP，还可以学习推理逻辑程序(ALPS)。在阿尔卑斯山中发现新的诱导物的新技术也可以应用于在非单调推理中学习偏好知识的方法。

In most previous work on Inductive Logic Programming (ILP), definite Horn programs or classical clausal programs are considered in the form of learned logic programs. However, research on knowledge representation in AI, in particular work on nonmonotonic reasoning, has shown that such monotonic programs are not adequate to represent our commonsense knowledge. To learn default rules or concepts in taxonomic hierarchy, a learning mechanism that deals with nonmonotonic reasoning is necessary. We propose a learning system LELP that learns Extended Logic Programs (ELPs). An ELP allows two kinds of negation, and can represent incomplete knowledge. LELP can learn default rules with exceptions in the form of ELPs, given incomplete positive and negative examples and background knowledge. In LELP, hierarchical defaults can also be learned by recursively calling the exception identification algorithm. Moreover, when some instances are possibly classified as both positive and negative, nondeterministic rules can also be learned.In this research, we further developed methodologies to learn ELPs. The contributions of the research can be summarized as follows.1.We proved the correctness of LELP, and analyzed its properties.2.We implemented various versions of LELP, by applying both top-down and bottom-up algorithms for producing general rules. To speed up the system, we also reimplemented LELP in Java, applied Genetic Algorithm to LELP, and automatically generated the search bias.3.We considered an extension of LELP which can learn not only ELPs but also Abductive Logic Programs (ALPs). The new technique to discover new abducibles in ALPs can also be applied to a method to learn preference knowledge in nonmonotonic reasoning.