归纳逻辑程序设计中的优化问题研究

归纳逻辑程序设计的核心问题是如何从背景知识中优选谓词构造满足约束的归纳假设,按Ｏｃｃａｍ准则,满足约束的最精简归纳假设为优,但迄今归纳逻辑程序设计中精简归纳假设构造的计算复杂性尚未解决。     

递归逻辑程序的强构造分层学习算法及其实现

本文提出了一种逻辑程序的强构造分层学习算法用该算法解决了一类递归逻辑程序的强构造二层学问题，为强的构造学习研究提供了一个新的思路，并就有关问题进行了详细的讨论。     

基于归纳的机器学习方法的实现研究

一、引言机器学习是解决计算机获取知识问题的方法。在这项研究中,对人类学习方法的模拟是一条重要的途径,而归纳正是人类学习的重要方式之一。从归纳中获取知识是人类新知识的极其重要的来源,它通过对经验材料的研究,探求新知识。由于一切科学理论的基本前提最初都是从经验事实中总结出来的,而从经验到一般规律的过程中,归纳推理起着不可替代的作用。开普勒从各大行星的天文观测     

构造性归纳综述

1.引言机器学习是人工智能领域的一个长期的研究热点,其研究目标,就是利用计算机来模拟、实现各种形式的学习行为。学习涉及两方面的内容:知识获取和技能获取。前者是从外界获得新的信息,后者是完善已得到的知识。对于这两方面在机器学习领域中有两种主要的学习典范。一种称为符号经验式学习——SEL(Symbolic Empirical Learning)——研究知识获取的本质问题,这种方法试图从事先提供的概念例子中分析出概念相应的规则,或称为描述。这是一种从具体到一般的推理过程,我们称之为归纳,因     

分布式并行约束归纳逻辑程序设计研究*

CILP是关系数据挖掘的主要技术之一。为提高CILP系统的效率,提出了一种基于C3模型,元学习技术和主从式静态负载平衡策略的分布式并行CILP算法,并实现了一个基于COW机群结构的分布式并行CILP原型系统。实验表明该算法是高效的,能获得较好的负载平衡,较高的加速比和并行效率。     

Prolog逻辑程序的测试方法

Prolog逻辑程序设计语言具有不同于传统程序设计语言的特点.本文根据测试Prolog程序的实践,提出了一系列为Prolog程序生成测试用例的准则.按这些准则测试实际程序效果良好.这是在逻辑程序测试新领域迈出的一步.     

逻辑程序的一种并行执行模型

本文基于静态相关性分析和动态调整相结合的方法，提出了一种逻辑程序的执行模型，它不仅开发了“与“并行，同进也开发了一定的“或“并行，从而有效地加速了逻辑程序的执行。     

修正的逻辑程序知识库更新方法

基于逻辑程序的知识库更新方法研究的焦点在于处理知识库的冲突问题,但代价是更新时规则库增大很快,该文提出了“修正的逻辑程序知识库更新方法”,此种方法基于一种规范知识库更新的形式化方法—修正程序,此种更新方法不仅可以最大程度地减少更新时规则库的增大,也避免了重复工作和知识库信息的丢失,还可以同时满足“替换更新”和“丰富更新”。     

Machine Learning and the foundations of inductive inference

The problem of valid induction could be stated as follows: are we justified in accepting a given hypothesis on the basis of observations that frequently confirm it? The present paper argues that this question is relevant for the understanding of Machine Learning, but insufficient. Recent research in inductive reasoning has prompted another, more fundamental question: there is not just one given rule to be tested, there are a large number of possible rules, and many of these are somehow confirmed by the data — how are we to restrict the space of inductive hypotheses and choose effectively some rules that will probably perform well on future examples? We analyze if and how this problem is approached in standard accounts of induction and show the difficulties that are present. Finally, we suggest that the explanation-based learning approach and related methods of knowledge intensive induction could be, if not a solution, at least a tool for solving some of these problems.     

The strong semantics for logic programs

Recently, the well-founded semantics of a logic programP has been strengthened to the well-founded semantics-by-case (WF_C) and this in turn has been strengthened to the extended well-founded semantics (WF_E). Both WF_C(P) and WF_E(P) have thelogical consequence property, namely, if an atomAj is true in the theory Th(P), thenAj is true in the semantics as well. However, neither WF_C nor WF_E has the GCWA property, i.e., if an atomAj is false in all minimal models ofP,Aj may not be false in WF_C(P) (resp. WF_E(P)). We extend the ideas in WF_C and WF_E to define a strong well-founded semantics WF_S which has the GCWA property. The strong semantics WF_S(P) is defined by combining GCWA with the notion ofderived rules. Here we use a new Type-III derived rules in addition to those used in WF_C and WF_E. The relationship between WF_S and WF_C is also clarified.     

简单单调约束逻辑程序的良基语义研究

在介绍约束逻辑程序的相关概念的基础上,研究了简单单调约束逻辑程序约束原子的正文字前缀幂集展开方法,并证明展开后的正规逻辑约束与约束逻辑程序的等价特性.分析了正规逻辑程序的交替不动点良基模型建立的原理,将简单单调约束逻辑程序等价展开为与其等价的正规逻辑程序,以求展开后的逻辑程序中的给定算子的最小不动点为切入,给出了简单单调约束逻辑程序的交替不动点的良基模型.论证了文中提出的简单单调约束逻辑程序良基模型定义的合理性,说明把约束逻辑程序转化为正规逻辑程序是可行的.     

An industrial visual inspection system that uses inductive learning

This paper presents an industrial visual inspection system that uses inductive learning. The system employs RULES-3 inductive learning algorithm to extract the necessary set of rules and template matching technique to process an image. Twenty 3×3 masks are used to represent an image. Each example consists of 20 frequencies of each mask. The system was tested on five different types of tea or water cups in order to classify the good and bad items. The system was trained using five good cups and then tested for 113 unseen examples. The results obtained showed the high performance of the system: the efficiency of the system for correctly classifying unseen examples was 100%. The system can also decide what type of the cup is being processed.     

Self-learning inductive inference machines

Self-knowledge is a concept that is present in several philosophies. In this article, we consider the issue of whether or not a learning algorithm can in some sense possess self-knowledge. The question is answered affirmatively. Self-learning inductive inference algorithms are taken to be those that learn programs for their own algorithms, in addition to other functions.
La connaissance de soi est un concept qui se retrouve dans plusieurs philosophies. Dans cet article, les auteurs s'interrogent à savoir si un algorithme d' apprentissage peut dans une certaine mesure posséder la connaissance de soi. lis apportent une reponse positive a cette question. Les algorithmes d'inference inductive autodidactes sont ceux qui font l'apprentissage de programmes pour leurs propres algorithmes, en plus d' autres fonctions.     

ALLPAD: approximate learning of logic programs with annotated disjunctions

Logic Programs with Annotated Disjunctions (LPADs) provide a simple and elegant framework for representing probabilistic knowledge in logic programming. In this paper we consider the problem of learning ground LPADs starting from a set of interpretations annotated with their probability. We present the system ALLPAD for solving this problem. ALLPAD modifies the previous system LLPAD in order to tackle real world learning problems more effectively. This is achieved by looking for an approximate solution rather than a perfect one. A number of experiments have been performed on real and artificial data for evaluating ALLPAD, showing the feasibility of the approach. Editors: Stephen Muggleton, Ramon Otero, Simon Colton.     

Most specific logic programs

More specific versions of definite logic programs are introduced. These are versions of a program in which each clause is further instantiated or removed and which have an equivalent set of successful derivations to those of the original program, but a possibly increased set of finitely failed goals. They are better than the original program because failure in a non-successful derivation may be detected more quickly. Furthermore, information about allowed variable bindings which is hidden in the original program may be made explicit in a more specific version of it. This allows better static analysis of the program's properties and may reveal errors in the original program. A program may have several more specific versions but there is always a most specific version which is unique up to variable renaming. Methods to calculate more specific versions are given and it is characterized when they give the most specific version.