一种基于Rough集获取规则知识的增量式学习方法

将Rough集理论应用于规则归纳系统,提出了一种基于粗糙集获取规则知识库的增量式学习方法,能够有效处理决策表中不一致情形,采用启发式算法获取决策表的最简规则,当新对象加入时在原有规则集基础上进行规则知识库的增量式更新,避免了为更新规则而重新运行规获取算法。并用UCI中多个数据集从规则集的规则数目、数据浓缩率、预测能力等指标对该算法进行了测试。实验表明了该算法的有效性。     

初中几何专家系统中的知识获取及实现

描述了初中几何专家系统中知识获取及实现的一般过程,指出了知识获取及实现中的难点和重点.由于研究问题的复杂性,专家系统规则库中规则量往往十分庞大,这给规则库的管理和维护带来很大不便.专家系统知识库的冗余性是影响系统运行效率和知识库维护的一个重要方面,针对一个具体的专家系统--平面几何智能解题系统,分析了关于知识库规则生成时效率低的问题,然后利用基于粗糙集的约简理论来消除和减少规则库的冗余,使得系统规则库中的规则精炼、简洁,易于维护,同时大大提高了系统的效率.     

初中几何专家系统中的知识获取及实现

描述了初中几何专家系统中知识获取及实现的一般过程,指出了知识获取及实现中的难点和重点。由于研究问题的复杂性,专家系统规则库中规则量往往十分庞大,这给规则库的管理和维护带来很大不便。专家系统知识库的冗余性是影响系统运行效率和知识库维护的一个重要方面,针对一个具体的专家系统——平面几何智能解题系统,分析了关于知识库规则生成时效率低的问题,然后利用基于粗糙集的约简理论来消除和减少规则库的冗余,使得系统规则库中的规则精炼、简洁,易于维护,同时大大提高了系统的效率。     

根据观察获取知识

本文介绍一种建立知识库的学习方法。从人类专家吸取知识的知识获取系统有两种类型:交互式和非交互式的。本文介绍非交互式知识获取系统,它依据观察从人类专家获得知识。这种知识获取系统学习人类专家用以解决问题的策略和从瞬时序列数据中抽取逻辑规则,其学习方法是基于翻译的学习(IBL),在学习过程中要用到预备知识。IBL 有两个子系统:翻译系统和学习系统。前者负责把现实世界的信息翻译成内部规则形式,而规则维护系统负责生成和界定知识。本文将介绍翻译系统和规则生成的预处理过程。     

一种基于任务分解的多知识库协同求解专家系统

针对特定领域的知识特点、知识表示方法及采用的推理模型,提出一种基于产生式规则的多知识库专家系统.该系统改进传统专家系统的框架设计,根据求解问题的类别划分将知识库分解成相应的子知识库,再将子知识库的知识规则按知识表示的深度加以分解,建立反映专家经验知识的浅层知识库和原理性知识的深层知识库.系统采用主推理机和从推理机二级推理方式,不同的子知识库采用相应的从推理机.从而任务单一,搜索范围减小,能快速形成待检目标集.主从推理机制与正反向推理结合,提高系统的推理效率.运用该系统模型建造的农业领域专家系统实例,运行效率得到改善,速度显著提高.     

智能知识获取方法研究

本文根据故障诊断专家系统及产生式规则的特点,提出了一种新的基于遗传算法的知识获取方法,通过这种方法,故障诊断专家系统的知识库可由故障事例自动生成     

计算机硬件维修知识库的知识表示和知识获取

本文以计算机硬件售后的维修服务为模型,为计算机硬件出现的故障及对应解决方案创建知识库.硬件故障知识采用事例表示法和产生式规则表示法两种方法,并将知识分为事例知识和规则知识两类.在事例知识的获取方面采用了自动获取和人工干预两种形式,在规则知识的获取方面采用的是人工干预形式.     

粗糙集理论在故障诊断规则获取中的应用

本文的目的是给出一种利用粗糙集理论解决故障诊断的规则获取问题的方法 ,该方法的特点是可以处理由于类重叠引起的样本信息不精确、不一致情况下的规则获取 .以规则形式表示的知识接近于人脑推理过程 ,因此基于规则的诊断方法在故障诊断中得到广泛使用 ,但规则获取是其瓶颈之一 .粗糙集 (RS)理论是为开发自动规则生成系统而提出的 ,其主要思想是在保持分类能力不变的前提下 ,通过知识约简 ,导出概念的分类规则 .因此 ,可以把 RS理论用于规则的故障诊断中 .本文给出了基于决策矩阵和决策函数的获取规则方法的流程图 ,以故障诊断实例说明其使用方法 ,并验证了其有效性     

一个领域自然语言理解的知识获取工具的设计实现

本文介绍了一个基于知识的自然语言理解系统和系统的知识库中知识的表示模式,描述了知识库中领域过程树的知识获取过程。根据领域过程树的知识表示模式,本文设计并实现了一个知识获取工具,很好地提高了领域过程树知识获取的速度和准确度。     

专家系统中基于粗集的知识获取、更新与推理

知识获取、知识更新和不确定性推理是设计专家系统的重要方面。根据粗集理论，提出了一种专家系统的结构模型，该系统在规则获取的基础上，利用系统运行的实例增量式地更新知识库中的规则及其参数，以改善系统的性能，利用知识库中的规则及数量参数进行不确定性推理，得出结论的可信度。     

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.     

Integrated graphical approach to knowledge representation and acquisition

An approach toward improving the accessbility of the knowledge and information structures of expert systems is described; it is based upon a foundation development environment called the Rule-Based Frame System (RBFS), which forms the kernel of a larger system, IDEAS. RBFS is a knowledge representation language, within which a distinction is drawn between information which represents the world or domain, and knowledge which states how to make conclusions based upon the domain. Information takes the form of frames, for system processing, but is presented to the user/developer as an associative network via a Visual Editor for the Generation of Associative Networks (VEGAN). Knowledge takes the form of production rules, which are connected at suitable points in the domain model, but again it is presented to the user via a graphical interface known as the Knowledge Encoding Tool (KET). KET is designed to assist in knowledge acquisition in expert systems. It uses a combination of decision support trees and associative networks as its representation. A combined use of VEGAN and KET will enable domain experts to interactively create and test their knowledge base with minimum involvement on behalf of a knowledge engineer. An inclusion of learning features in VEGAN/KET is desirable for this purpose. The main objective of these tools, therefore, is to encourage rapid prototyping by the domain expert. VEGAN and KET are implemented in the Poplog environment on SUN 3/50 workstations.     

基于着色Petri网模糊专家系统的研究

针对变电站无功控制模糊专家系统知识表示不确定性及规则数量多的特点,文章以模糊、着色Petri网为基础,提出了一种基于模糊着色Petri网的知识表示与规则获取方法。该方法利用Petri网的图形化环境特点,将模糊规则库的不同变量用不同的颜色加以区分,不同规则中的同一个变量用该变量的颜色集表示,构成一个模糊着色Petri网模型。充分利用着色Petri网的特点,对推理过程进行了仔细研究,并提出一种基于着色模糊Petri网的启发式搜索策略。将其用于变电站无功控制的模糊专家系统中,结果表明,基于着色Petri网的模糊知识表示和获取方法,对于大型、复杂变电站模糊专家控制系统是非常有效的。     

Using natural language sources in model-based knowledge acquisition

This paper introduces a well defined co-operation between domain expert, knowledge engineer, and knowledge acquisition and transformation tools. First, the domain expert supported by a hypertext tool generates an intermediate representation from parts of authentic texts of a domain. As a side effect, this representation serves as human readable documentation. In subsequent steps, this representation is semi-automatically transformed into a formal representation by knowledge acquisition tools. These tools are fully adapted to the expert's domain both in terminology and model structure which are developed by the knowledge engineer from a library of generic models and with preparation tools.     

A Delphi-based approach to developing expert systems with the cooperation of multiple experts

Knowledge acquisition has been a critical bottleneck in building knowledge-based systems. In past decades, several methods and systems have been proposed to cope with this problem. Most of these methods and systems were proposed to deal with the acquisition of domain knowledge from single expert. However, as multiple experts may have different experiences and knowledge on the same application domain, it is necessary to elicit and integrate knowledge from multiple experts in building an effective expert system. Moreover, the recent literature has depicted that “time” is an important parameter that might significantly affect the accuracy of inference results of an expert system; therefore, while discussing the elicitation of domain expertise from multiple experts, it becomes an challenging and important issue to take the “time” factor into consideration. To cope with these problems, in this study, we propose a Delphi-based approach to eliciting knowledge from multiple experts. An application on the diagnosis of Severe Acute Respiratory Syndrome has depicted the superiority of the novel approach.     

Incremental acquisition of search knowledge

The development of highly effective heuristics for search problems is a difficult and time-consuming task. We present a knowledge acquisition approach to incrementally model expert search processes. Though, experts do not normally have complete introspective access to that knowledge, their explanations of actual search considerations seem very valuable in constructing a knowledge-level model of their search processes.Furthermore, for the basis of our knowledge acquisition approach, we substantially extend the work done on Ripple-down rules which allows knowledge acquisition and maintenance without analysis or a knowledge engineer. This extension allows the expert to enter his domain terms during the KA process; thus the expert provides a knowledge-level model of his search process. We call this framework nested ripple-down rules.Our approach targets the implicit representation of the less clearly definable quality criteria by allowing the expert to limit his input to the system to explanations of the steps in the expert search process. These explanations are expressed in our search knowledge interactive language. These explanations are used to construct a knowledge base representing search control knowledge. We are acquiring the knowledge in the context of its use, which substantially supports the knowledge acquisition process. Thus, in this paper, we will show that it is possible to build effective search heuristics efficiently at the knowledge level. We will discuss how our system SmS1.3 (SmS for Smart Searcher) operates at the knowledge level as originally described by Newell. We complement our discussion by employing SmS for the acquisition of expert chess knowledge for performing a highly pruned tree search. These experimental results in the chess domain are evidence for the practicality of our approach.     

Knowledge map: An approach to knowledge acquisition in developing engineering expert systems

This paper addresses the critical issues of knowledge acquisition in developing knowledge-based expert systems for engineering tasks. First, it reviews the role of knowledge acquisition and its current practice in expert system development. Then, a new approach based on three stages of knowledge refinement is suggested to improve the process of knowledge acquisition. This approach, calledrule verification without rule construction, is proposed to allow knowledge engineers and domain experts to experience a more intimate and balanced role in developing intelligent systems. The communication tool developed for this concept is calledknowledge map, which provides a systematic way of indexing and quantifying a piece of knowledge in the problem space by defining important attributes as the axes of the map. This approach is demonstrated by constructing a twodimensional map for a knowledge-based engineering design system, IDRILL, which we are currently developing. Future expansions of this knowledge acquisition technique are summarized as the conclusions of this paper.This paper was presented in part at the 1986 ASME International Computers in Engineering Conference in Chicago, IL, and appeared in the proceedings of that conference.     

Expert knowledge acquisition and the unwilling expert: a knowledge engineering perspective

Expert systems are an evolving technology with the potential to make human expertise widely and cheaply available. The literature describing the development of expert systems generally assumes that experts willingly give up their knowledge. This is unrealistic and may be a reason why most expert system projects fail. This paper explores the problem of unwilling experts from the perspective of a knowledge engineer building an expert system. The link between knowledge and organizational power is established and human motivation theories are discussed. Finally, a new motivational approach is introduced to help the knowledge engineer deal with unwilling experts.     

Knowledge acquisition as a constructive modeling activity

Knowledge acquisition is a constructive modeling process, not simply a matter of “expertise transfer.” Consistent with this perspective, we advocate knowledge acquisition practices and tools that facilitate active collaboration between expert and knowledge engineer, that exploit a serviceable theory in their application, and that support knowledge-based system development from a life-cycle perspective. A constructivist theory of knowledge is offered as a plausible theoretical foundation for knowledge acquisition and as an effective practical approach to the dynamics of modeling. In this view, human experts construct knowledge from their own personal experiences while interacting with their social constituencies (e.g., supervisors, colleagues, clients patients) in their niche of expertise. Knowledge acquisition is presented as a cooperative enterprise in which the knowledge engineer and expert collaborate in constructing an explicit model of problem solving in a specific domain. From this perspective, the agenda for the knowledge acquisition research community includes developing tools and methods to aid experts in their efforts to express, elaborate, and improve their models of the domain. This functional view of expertise helps account for several problems that typically arise in practical knowledge acquisition projects, many of which stem directly from the inadequacies of representations used at various stages of system development. to counter these problems, we emphasize the use of mediating representations as a means of communication between expert and knowledge engineer, and intermediate representations to help bridge the gap between the mediating representations themselves, as well as between the mediating representations and a particular implementation formalism. © 1993 John Wiley & Sons, Inc.     

Causal domain model driven knowledge acquisition for expert diagnosis system development

Despite the successful operation of expert diagnosis systems in various areas of human activity these systems still show several drawbacks. Expert diagnosis systems infer system faults from observable symptoms. These systems usually are based on production rules which reflect so called shallow knowledge of the problem domain. Though the explanation subsystem allows the program to explain its reasoning, deeper theoretical justifications of program's actions are usually needed. This may be one of the reasons why in recent years in knowledge engineering there has been a shift from rule-based systems to model-based systems. Model-based systems allow us to reason and to explain a system's physical structure, functions and behaviour, and thus, to achieve much better understanding of the system's operations, both in normal mode and under fault conditions. The domain knowledge captured in the knowledge base of the expert diagnosis system must include deep causal knowledge to ensure t he desired level of explanation. The objective of this paper is to develop a causal domain model driven approach to knowledge acquisition using an expert–acquisition system–knowledge base paradigm. The framework of structural modelling is used to execute systematic, partly formal model-based knowledge acquisition, the result of which is three structural models–one model of morphological structure and two kinds of models of functional structures. Hierarchy of frames are used for knowledge representation in topological knowledge base (TKB). A formal method to derive cause–consequence rules from the TKB is proposed. The set of cause–consequence rules reflects causal relationships between causes (faults) and sequences of consequences (changes of parameter values). The deep knowledge rule base consists of cause–consequence rules and provides better understanding of system's operation. This, in turn, gives the possibility to construct better explanation fa cilities for expert diagnosis system. The proposed method has been implemented in the automated structural modelling system ASMOS. The application areas of ASMOS are complex technical systems with physically heterogeneous elements.