Classification Accuracy: Machine Learning vs. Explicit Knowledge Acquisition

This empirical study provides evidence that machine learning models can provide better classification accuracy than explicit knowledge acquisition techniques. The findings suggest that the main contribution of machine learning to expert systems is not just cost reduction, but rather the provision of tools for the development of better expert systems.     

Task-Structures,Knowledge Acquisition and Learning

One of the old saws about learning in AI is that an agent can only learn what it can be told, i.e., the agent has to have a vocabulary for the target structure which is to be acquired by learning. What this vocabulary is, for various tasks, is an issue that is common to whether one is building a knowledge system by learning or by other more direct forms of knowledge acquisition. I long have argued that both the forms of declarative knowledge required for problem solving as well as problem-solving strategies are functions of the problem-solving task and have identified a family of generic tasks that can be used as building blocks for the construction of knowledge systems. In this editorial, I discuss the implication of this line of research for knowledge acquisition and learning.     

Product Evaluation Knowledge Acquisition Based on Rough Sets

1 Introduction Acquiring knowledge is constantly encountered in the mechanical product evaluation process, such as during setting up evaluation index system and determining evaluation knowledge. The most useful expression form of knowledge is production rule whenever concerning the field knowledge or decision knowledge. How to effectively acquire and express the knowledge becomes the issue that the evaluation work must solve at first. One of an important application of the rough set theory is …     

An Analysis of Knowledge Acquisition Roles and Participants

Much recent research effort in the field of knowledge acquisition (KA) has focussed on extending knowledge acquisition techniques and processes to include a wider array of participants and knowledge sources in a variety of knowledge acquisition scenarios. As the domain of expert systems applications and research has expanded, techniques have been developed to acquire and incorporate knowledge from groups of experts and from various sources such as text, video, and audio tapes. However, the dominant participant-role model remains that of the knowledge engineer eliciting knowledge from one or more human experts. This conceptual gap has contributed to the major divisions in the KA field between researchers interested in manual KA and those developing tools for automated KA. This article considers the wide variety of possible KA scenarios and presents a meta-view of KA participants and the roles they may assume.We suggest that it is more appropriate to think of knowledge acquisition participants as playing one or more roles. These include knowledge sources, agents and targets for KA processes. We also present a participant model drawn from research in decision support systems that more accurately characterizes the diversity of the entities participating in the KA process. This view is more inclusive as it allows us to consider both human-human and human-computer KA interactions as well as the whole variety of knowledge sources and targets. A careful consideration of the meta-view and its associated role-participant mappings also yields the new ideas of the elemental and composite role and the multi-role entity. These new constructs are then used to identify areas where research is currently needed and to generate specific research issues. Taken altogether, this view allows a more flexible consideration of the many possible combinations that can and frequently do occur in actual KA situations.     

A Survey of Commonsense Knowledge Acquisition

Collecting massive commonsense knowledge (CSK) for commonsense reasoning has been a long time standing challenge within artificial intelligence research. Numerous methods and systems for acquiring CSK have been developed to overcome the knowledge acquisition bottleneck. Although some specific commonsense reasoning tasks have been presented to allow researchers to measure and compare the performance of their CSK systems, we compare them at a higher level from the following aspects: CSK acquisition task (what CSK is acquired from where), technique used (how can CSK be acquired), and CSK evaluation methods (how to evaluate the acquired CSK). In this survey, we first present a categorization of CSK acquisition systems and the great challenges in the field. Then, we review and compare the CSK acquisition systems in detail. Finally, we conclude the current progress in this field and explore some promising future research issues.     

知识获取研究

知识获取是知识从外部知识源到计算机内部的转换过程,是当前知识工程研究的热点和难点问题之一。该文阐述了知识获取的定义、方法和最终目标,重点介绍了显性知识和隐性知识的获取方法,并分析了这两类方法的优缺点,最后给出了知识获取的困难和一些改进思想。     

农业智能系统的知识获取技术研究

作者及其团队长期针对农业领域的知识获取技术进行了系列性研究.阐述了运用智能引导、机器学习、数据挖掘、智能计算等技术的人工和自动/半自动的知识获取方法.这些方法能够有效地获取领域知识,发现隐含模式,进行知识精化.研发了知识获取工具.这些方法和工具反映了知识获取技术对农业信息工程所起的重要作用.     

基于模拟退火算法的知识获取方法的研究

从优化角度提出了从事例中获取知识的机器学习方法。该方法利用模拟退火算法,按照预定的优化目标,从事例中生成最优的产生式规则,给出其算法,并以旋转机械故障诊断知识获取为例,阐述了基于模拟退火算法的知识获取机制及其实现方法。     

一种改进的规则知识获取方法

知识获取是建立专家系统的最基本最重要的过程,但它又是研制和开发专家系统的“瓶颈”。文章提出了一种改进的规则知识机器自动获取技术,它将学习看作是在一个符号描述空间中的启发式搜索过程,能够通过归纳从专家决策的例子中确定决策规则,从而大大简化了从专家到机器的知识转换过程。     

基于专利的知识获取系统研究

应用现有的数据库技术、专利知识以及专家系统技术,研制了一个基于专利的知识获取系统,方便了以数据库为载体的专利知识系统的管理维护一体化。     

基于机器学习的服装款式知识库设计

基于机器学习的服装制造流程管理与控制系统的开发应用,可以摆脱长期以来服装制造流程的手工操作方式,缩短生产周期,达到智能化制衣的目的。而基于机器学习的服装智能化制造的实现又依赖于服装制造流程中各环节所对应的服装知识库信息的支撑。款式设计作为服装制造流程中不可缺少的一部分同样也依赖于款式知识库中信息的支撑。本文主要描述款式知识库的设计原理、设计方法和它的应用。     

一种专家系统知识获取时的属性约简算法

知识获取是构造专家系统的“瓶颈”,提供准确的推理知识是进行决策规划的关键。文中运用粗糙集理论,通过粗糙集的约简消除冗余的条件属性,实现对知识库的精简。首先研究知识获取,在阐明知识的层次结构基础上,给出了概念化、形式化、知识库求精三个知识获取过程;然后研究属性约简算法,在研究集合差异度和属性的重要性、约简算法推导过程的基础上,给出了属性约简算法的六个步骤。最后根据属性约简算法及其步骤,对功能点分析法构建软件成本估算专家系统时,组成技术复杂因子的14个因素进行了约简。     

Learning Premises of Fuzzy Rules for Knowledge Acquisition in Classification Problems

A key issue in building fuzzy classification systems is the specification of rule conditions, which determine the structure of a knowledge base. This paper presents a new approach to automatically extract classification knowledge from numerical data by means of premise learning. A genetic algorithm is employed to search for premise structure in combination with parameters of membership functions of input fuzzy sets to yield optimal conditions of classification rules. The major advantage of our work is that a parsimonious knowledge base with a low number of rules can be achieved. The practical applicability of the proposed method is examined by computer simulations on two well-known benchmark problems of Iris Data and Cancer Data classification. Received 11 February 1999 / Revised 13 January 2001 / Accepted in revised form 13 February 2001     

PLATA: An Application of LEGAL, a Machine Learning Based System, to a Typology of Archaeological Ceramics

The authors here show that machine learning techniques can be used for designing an archaeological typology, at an early stage when the classes are not yet well defined. The program (LEGAL, LEarning with GAlois Lattice) is a machine learning system which uses a set of examples and counter-examples in order to discriminate between classes. Results show a good compatibility between the classes such as the yare defined by the system and the archaeological hypotheses. This revised version was published online in July 2006 with corrections to the Cover Date.     

Improved Rooftop Detection in Aerial Images with Machine Learning

In this paper, we examine the use of machine learning to improve a rooftop detection process, one step in a vision system that recognizes buildings in overhead imagery. We review the problem of analyzing aerial images and describe an existing system that detects buildings in such images. We briefly review four algorithms that we selected to improve rooftop detection. The data sets were highly skewed and the cost of mistakes differed between the classes, so we used ROC analysis to evaluate the methods under varying error costs. We report three experiments designed to illuminate facets of applying machine learning to the image analysis task. One investigated learning with all available images to determine the best performing method. Another focused on within-image learning, in which we derived training and testing data from the same image. A final experiment addressed between-image learning, in which training and testing sets came from different images. Results suggest that useful generalization occurred when training and testing on data derived from images differing in location and in aspect. They demonstrate that under most conditions, naive Bayes exceeded the accuracy of other methods and a handcrafted classifier, the solution currently used in the building detection system.     

Knowledge Acquisition Advisor (KA): An interactive knowledge-acquisition tool for expert system development

A new interactive knowledge acquisition tool, called Knowledge Acquisition Advisor (KA²), is presented in this paper. The new tool will help knowledge engineers to conduct effective knowledge-elicitation interviews with domain experts through structured knowledge acquisition for both analytic and synthetic problems. A graphic modeling data structure, called Knowledge Graph is proposed, which allows knowledge engineers to model domain problems with their images and understanding. By using Knowledge Graph, knowledge engineers are able to decompose a domain problem into several components, to model the feature of each component, and to explore their relations by linking them with sets of questions. These questions can later be employed to guide the KA interview. Moreover, KA² is particularly useful for interview through computer networks, so the knowledge acquisition can take place remotely.     

基于虚拟样机的故障知识获取研究

文章利用CATIA软件对某型导弹发射装置发射台建模并利用工程分析模块Analysis＆Simulation进行有限元分析,获得导弹发射装置在载荷变化情况下的可视化资料,获取载荷一故障之间的对应关系,建立智能诊断专家系统知识库,为解决机电设备故障诊断专家系统知识获取的“瓶颈”问题提供了一个新的思路。     

基于Web的本体学习模型的设计与实现

对本体(ontology)的研究在计算机科学领域变的越来越广泛,但手工构建本体是一项繁琐而辛苦的任务,还容易导致知识获取瓶颈,无法保持本体的更新。本体学习技术是利用本体工程技术和机器学习技术等众多学科技术来实现本体的自动或半自动构建。该文提出了基于Web的本体学习模型,分析了模型实现中的文档预处理、术语抽取、概念选择、概念分类等关键技术。