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     

归纳逻辑及其在归纳学习中的应用问题

逻辑主义对人工智能具有重大的意义,但也有其局限性(特别是对于归纳的模拟),现代归纳逻辑作为一类非标准逻辑应成为归纳学习逻辑基础的重要组成部分,对此值得进一步加以探讨。     

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     

启发式知识获取方法研究

归纳学习是解决知识自动获取的有效方法，针对ID3算法、基于粗集的归纳学习以及其它一些归纳学习方法存在的问题，提出了一种新的归纳学习算法ITIL。此算法用信息增益为启发式，选择尽量少的重要属性或组合，以可分辨性为依据提取规则，许多实例表明，这些规则不仅简单，而且冗余小，作为知识获取模块的一部分，ITIL已被集成到一个“基于知识发现的医疗诊断辅助系统”动态知识库子系统中。     

一种新的决策树归纳学习算法

本文不示例学习的重要分枝－－决策树归纳学习进行了分析探讨，从示例学习最优化的角度分析了决策树归纳学习的优化原则，指出了以往的以ＩＤ３为代表的归纳学习算法所固有的缺陷，并提出了一种新的基于概率的决策树归纳学习算法ＰＩＤ，ＰＩＤ在扩展属性的选择上仍采用基于信息增益率的方法，但在树上的扩展过程中，采用属性聚类的方法进行树的支合并。ＰＩＤ得到的决策树在树的规模和分类精度上都优于ＩＤ３。     

范例推理技术在银行客户信用评估中的应用

范例推理技术是人工智能领域中一种基于知识的问题求解和学习方法。为了有效评估银行客户信用等级并提高银行信贷业务效率，文中提出了范例推理技术(CBR)在银行客户信用评估中的应用，并给出了基于范例推理的银行客户信用评估系统的原型，介绍了该系统中的关键技术：范例表示、相似性计算和范例检索，研究了归纳学习、特征子集选择等机器学习方法在范例检索中的应用。     

LP在语义Web数据挖掘研究中的应用

语义网络数据挖掘是基于语义网络环境的数据挖掘，它给数据挖掘技术的应用研究提出了新的课题。归纳逻辑程序设计是由机器学习与逻辑程序设计交叉所形成的一个研究领域，它为知识工程等人工智能的应用领域提供了新的强有力的技术支持。分析了现有几种常用数据挖掘技术在语义Web环境下应用的局限性，提出了采用归纳逻辑程序设计（ILP）作为语义Web上适合的数据挖掘技术，给出了应用这种技术的算法描述，通过具体实例验证了其可行性。     

从示例中获取结构化描述的知识一过程

文章提出了适合结构化知识获取的一种思想方法,并结合在一种简明而典型的应用领域内实现的过程获取系统ＫＢＭＬ,详细地阐述了这种思想的有关方面。它采用分级分阶段的匹配、归纳策略逐步从示教实例中获取一个完整的过程;把匹配和归纳有机地结合在一起是 ＫＢＭＬ的核心内容。     

面向属性的归纳与概念聚类

面向属性的归纳是新近提出的一种广泛用于数据库中的知识发现的方法，提出这种方法与一种机器学习方法－－概念聚类之间的紧密联系，并描述如何使用一个概念聚类算法进行面向属性的归纳。     

基于对象模型的关系模型设计方法

在数据库设计中,模式设计受到了广泛的重视。而设计出基于对象的Ｅ－Ｒ模型至今仍缺乏比较详细的有效的方法。本文进一步探讨概念设计的方法,提出了对象模型转换成关系模型的途径和算法,并给出了对象类获取的三种抽象机制：分类,聚合,归纳。     

Tickcon专家系统中的不确定性问题的研究

不确定性问题是人工智能和专家系统中的难点和核心研究课题，文章着重讨论Tickcon专家系统中的一个典型的不确定性问题的分析并提出了一个解决方案。     

定量问题求解专家系统——ESQPS

定量问题求解系统，是人工智能和专家系统研究领域的一个重要课题。本文介绍了ＥＳＱＰＳ－一个应用面向对象（ＯＯ）技术开发，可用于科学和工程技术领域中求解基本物理问题的专家系统，简述了该系统的知识表达和问题求解方法的设计思想和实现技术。     

Selection of knowledge acquisition techniques based upon the problem domain characteristics of production and operations management expert systems

The application of expert systems to various problem domains in business has grown steadily since their introduction. Regardless of the chosen method of development, the most commonly cited problems in developing these systems are the unavailability of both the experts and knowledge engineers and difficulties with the process of acquiring knowledge from domain experts. Within the field of artificial intelligence, this has been called the 'knowledge acquisition' problem and has been identified as the greatest bottleneck in the expert system development process. Simply stated, the problem is how to acquire the specific knowledge for a well-defined problem domain efficiently from one or more experts and represent it in the appropriate computer format. Given the 'paradox of expertise', the experts have often proceduralized their knowledge to the point that they have difficulty in explaining exactly what they know and how they know it. However, empirical research in the field of expert systems reveals that certain knowledge acquisition techniques are significantly more efficient than others in helping to extract certain types of knowledge within specific problem domains. In this paper we present a mapping between these empirical studies and a generic taxonomy of expert system problem domains. In so doing, certain knowledge acquisition techniques can be prescribed based on the problem domain characteristics. With the production and operations management (P/OM) field as the pilot area for the current study, we first examine the range of problem domains and suggest a mapping of P/OM tasks to a generic taxonomy of problem domains. We then describe the most prominent knowledge acquisition techniques. Based on the examination of the existing empirical knowledge acquisition research, we present how the empirical work can be used to provide guidance to developers of expert systems in the field of P/OM.     

A hybrid knowledge-based system applied to epidemic screening

Although many knowledge-based systems (KBSs) focus on single-paradigm approaches to encoding knowledge (such as production rules), experts rarely use a single type of knowledge in solving a problem. More often, an expert will apply a number of reasoning mechanisms. In recent years, rule-based reasoning (RBR), case-based reasoning (CBR) and model-based reasoning (MBR) have emerged as important and complementary reasoning methodologies in artificial intelligence. For complex problem solving, it is useful to integrate RBR, CBR and MBR. In this paper, a hybrid KBS which integrates a deductive RBR system, an inductive CBR system and a quantitative MBR system is proposed for epidemic screening. The system has been tested using real data, and results are encouraging.     

基于Prolog的专家系统研究与应用

随着计算机与网络通信技术的迅猛发展,特别是互联网的大规模普及,围绕人工智能与专家系统的研究和应用开发也迎来一个蓬勃发展的新时期。Prolog语言是人工智能与专家系统领域最著名的逻辑程序设计语言。本文基于Prolog平台上,探讨了专家系统建造的原理和应用。     

基于Prolog的专家系统研究与应用

随着计算机与网络通信技术的迅猛发展,特别是互联网的大规模普及,围绕人工智能与专家系统的研究和应用开发也迎来一个蓬勃发展的新时期。Prolog语言是人工智能与专家系统领域最著名的逻辑程序设计语言。本文基于Prolog平台上,探讨了专家系统建造的原理和应用。     

Inductive learning: Algorithms and frontiers

Machine learning is a major subfield of artificial intelligence. It has been seen as a feasible way of avoiding the knowledge bottleneck problem in knowledge-based systems development. Research on machine learning has concentrated in the main on inductive learning. This paper surveys the current inductive learning research. The three typical inductive algorithms, AQ11, ID3 and HCV, are summarized with their main features being analyzed and three research frontiers, i.e., constructive learning, incremental learning and learning from data bases, in inductive learning are introduced.     

基于产生式系统的知识建模

如何建立系统完整的专家知识模型,并将其快速映射为面向计算机推理的人工智能语言是当前专家系统研究的重点和难点,而知识表示是其中的关键.本文将模型驱动的思想引入到专家系统领域,定义了一种基于不确定性产生式知识表示的元模型,设计并实现了相应的图形化建模工具和转换引擎,并基于此提出一种新的产生式系统应用框架.该框架在某健康信息评估专家系统中获得了成功的应用.     

Inductive Learning

Machine learning(ML)is a major subfield of artificial intelligence(AI).It has been seen as a feasible way of avoiding the knowledge bottleneck problem in knowledge-based systems development.Research on ML has concentrated in the main on inductive learning,a paradigm for inducing rules from unordered sets of exmaples.AQ11 and ID3,the two most widespred algorithms in ML,are both inductive.This paper first summarizes AQ11,ID3 and the newly-developed extension matrix approach based HCV algorithm;and then reviews the recent development of inductive learing and automatic knowledge acquisition from data bases.