Integration of multiple knowledge representation for classification problems

This paper describes an approach to integrating various knowledge representations for classification problems. Knowledge representation forms have been analysed. The analysis shows that representation suitability depends on a given situation. Therefore, multiple representation form capability and form conversion capability are both necessary to support developing knowledge bases for wide application areas. A classification problem tool, called HOLON/VP(DT), has been developed with the aim of providing experts with the integrated knowledge representation capability. A knowledge base can be represented in a tabular form, a rule form and a tree form. Form conversion can be accomplished at all times. With this integrated representation, an expert is able to build a knowledge base using the most appropriate form.     

Procedural control in production systems

This paper proposes a general production system architecture that allows procedural control knowledge to be directly represented and used. This architecture, called a controlled production system, is based on a separately specified control structure that effects control over production invocation and interaction independently of the search strategy.It is shown that a controlled production system provides a basis for describing and implementing control constructs which, unlike most existing schemes, is formally adequate and retains all the properties desired of a knowledge based system—modularity, flexibility, extensibility and explanatory capacity. We also show that this architecture provides for a uniform programming methodology—the procedural languages and the declarative languages turn out to be special cases of a controlled production system.Schemes for improving system efficiency and resolving nondeterminism are also examined. It is shown that the separate representation of control provides a basis for a theory of efficiency transformations on production systems, and allows for more effective means of directing search.     

基于知识的设计流程控制

由于电子ＣＡＤ系统的复杂性不断增加，大量高度专业化的ＶＬＳＩ设计工具使设计的工具选择和启动过程变得十分繁琐和复杂。为了克服上述困难，本文提出一个基于知识推理的方法来解决这一问题。我们采用一种有向图和基于设计上下文的产生式规则来共同构造设计流程的模型。     

Prolog versus Kee — A case study

The selection of the software development tool for the development of an expert system is a difficult and often disputed decision. This paper describes a comparison of a knowledge engineering tool, Kee, and a general purpose language, Prolog, on concrete and real life example from AGATHA, an electronic circuit board diagnosis expert system.Prolog is a high-level programming language with flexible and powerful inference mechanisms. Kee is a big tool that supports a frame-based knowledge representation, an object-oriented programming style and a built-in rule system. It also offers a window environment suitable for rapid development of user-interface prototypes.Prolog's representation is more succinct, implicit and uses problem specific predicates and therefore leaves more room for personal programming styles. Kee is more verbose, explicit and uses standard templates. The maintainability of a Prolog implementation relies heavily on good documentation. In Kee, the unavoidable ‘escapes to Lisp’ require a maintainer to be fluent in Kee and Lisp.Both Prolog and Kee require a considerable investment in learning time.     

一个专家控制系统的Matlab程序实现

本文首先简述基于粗集方法的专家控制系统的基本构成、知识表达方式及学习推理方法,通过研究导师的知识与学习者的知识之间的依赖程度,提出了一个专家控制系统的生产过程中决策控制规律推理的方法,最后给出了利用Matlab实现该生产系统简化及决策控制规律推理的程序。     

专家系统开发环境NEW的设计与实现

本文分析了当前知识工程语言及其开发环境的现状,并介绍了基于知识工程语言及其支撑环境应是传统程序设计语言及其支撑环境的提高和进化的思想设计和实现的NEW专家系统开发环境,本文主要介绍了NEW的系统结构、知识表示、推理机制、开发环境以及实现与应用的情况。     

EXPERT-MM: A knowledge-based system for maintenance management

This paper presents an approach to the design and development of knowledge-based systems in general and their application in the field of maintenance management in particular. Our approach is based on the idea that different kinds of knowledge in a given domain, namely declarative, procedural and heuristic are supported by corresponding methods and software tools. A prototype knowledge-based system, called EXPERT-MM, for the maintenance activities in the Siam Gipsum Industry (Bangkok, Thailand) has been worked out as a case study and is described in the paper. EXPERT-MM supports three main functions: maintenance policy suggestions, machine diagnosis and maintenance scheduling. The maintenance policy deals with the three types of preventive maintenance. For each component of the equipment it analyses the historical failure data and recommends an appropriate policy with optimal preventive maintenance intervals. This is based on the experts' knowledge stored in a knowledge base. A rotary screw type air compressor is selected for a diagnosis. The knowledge representation scheme is rule-based and the inference strategy mechanism is backward chaining. The knowledge-acquisition process has been organized and realised using a decision tree diagram. The knowledge base contains 154 rules for the diagnosis and 54 rules for the maintenance model selection. the maintenance scheduling module is procedure based. EXPERT-MM development is based on the software tools dBase III Plus, TURBO PASCAL version 6.0 and expert system shell EXSYS, all integrated into a single software system with a user-friendly interface.     

A fuzzy expert database system

Fuzzy concepts always exist in much of human reasoning as well as decision making. This paper presents a fuzzy expert database system which is an integration of a fuzzy expert system building tool called SYSTEM Z-II and a database management system called Rdb/VMS. This system is able to extract fuzzy data and terms stored in a database and used in the fuzzy reasoning in an expert system. It can also retrieve information by fuzzy database-queries which are generated by the expert system automatically. Many expert systems in different domain areas such as decision making can be constructed. Sample applications are described to demonstrate the flexibility and power of this system. The fuzzy query language defined and used in the system can also be used independently as a fuzzy enquiry tool in database applications.     

An integrated case-based reasoning approach for intelligent help desk fault management

As modern business functions become more complex and knowledge-intensive, with increasing demands for quality services, there is an emerging trend for organisations to develop and deploy intelligent knowledge-based systems for mission-critical operations. Some of the challenges in successfully implementing this breed of systems depend on how well the intelligent system is integrated with conventional existing information systems and workflow, and the quality of the intelligent system itself. Developing quality expert systems lies in the effective modelling of cognitive processes of human experts and representation of various forms of related knowledge in a domain. An integrated intelligent system called the Intelligent Help Desk Facilitator (IHDF), has been developed for computer and network fault management. The system, which comprises various modules including an expert system, is successfully deployed in a problem response help desk environment of a local bank. This paper describes a cognitive-driven approach to the development of the expert system based on a hybrid knowledge representation and reasoning strategy. The approach incorporates a hybrid case-based reasoning (CBR) framework of techniques which include case memory organisation structures (discrimination networks and shared-featured networks), case indexing and retrieval schemes (fuzzy character-matching, nearest-neighbour similarity matching and knowledge-guided indexing); and an interactive and incremental style of reasoning. The paper discusses the design and implementation of the expert system component of IHDF and illustrates the appropriateness of the hybrid architecture for problem resolution and diagnostic types of applications.     

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.     

R1-soar: an experiment in knowledge-intensive programming in a problem-solving architecture

This paper presents an experiment in knowledge-intensive programming within a general problem-solving production-system architecture called Soar. In Soar, knowledge is encoded within a set of problem spaces, which yields a system capable of reasoning from first principles. Expertise consists of additional rules that guide complex problem-space searches and substitute for expensive problem-space operators. The resulting system uses both knowledge and search when relevant. Expertise knowledge is acquired either by having it programmed, or by a chunking mechanism that automatically learns new rules reflecting the results implicit in the knowledge of the problem spaces. The approach is demonstrated on the computer-system configuration task, the task performed by the expert system R1.     

Use of a domain model to drive an interactive knowledge-editing tool

The manner in which a knowledge-acquisition tool displays the contents of a knowledge base affects the way users interact with the system. Previous tools have incorporated semantics that allow knowledge to be edited in terms of either the structural representation of the knowledge or the problem-solving method in which that knowledge is ultimately used. A more effective paradigm may be to use the semantics of the application domain itself to govern access to an expert system's knowledge base. This approach has been explored in a program called OPAL, which allows medical specialists working alone to enter and review cancer treatment plans for use by an expert system called ONCOCIN. Knowledge-acquisition tools based on strong domain models should be useful in application areas whose structure is well understood and for which there is a need for repetitive knowledge entry.     

一个基于专家知识的不确定性分析推理系统

本文介绍EXDASS系统中进行不确定性分析的推理系统.该系统的工作方式是依据专 家对不确定性作出判断的有关知识信息,通过人机交互的动态过程以实现对决策的支持.专 家知识的量化表示及其推理机制的实现以可能性证据理论为基础.文中描述了系统的设计思 想和工作原理,重点介绍了在知识表示方面的自学习特性,并且给出了应用实例.     

VIBEX: an expert system for vibration fault diagnosis of rotating machinery using decision tree and decision table

This paper proposes an expert system called VIBEX (VIBration EXpert) to aid plant operators in diagnosing the cause of abnormal vibration for rotating machinery. In order to automatize the diagnosis, a decision table based on the cause-symptom matrix is used as a probabilistic method for diagnosing abnormal vibration. Also a decision tree is used as the acquisition of structured knowledge in the form of concepts is introduced to build a knowledge base which is indispensable for vibration expert systems. The decision tree is a technique used for building knowledge-based systems by the inductive inference from examples and plays a role itself as a vibration diagnostic tool. The proposed system has been successfully implemented on Microsoft Windows environment and is written in Microsoft Visual Basic and Visual C++. To validate the system performance, the diagnostic system was tested with some examples using the two diagnostic methods.     

概念图知识处理器的设计

概念图作为一种新的知识表示方法,有着广泛的应用前景。本文首先简要介绍了概念图知识表示方法,然后详细给出了一种适用于微型机的专家系统开发工具概念图知识处理器的设计和实现方案。     

The use of knowledge acquisition in instructional design

Expert systems and knowledge based systems have emerged from “esoteric” laboratory research in Artificial Intelligence (AI) to become an important tool for approaching real world problems. Expert systems are distinctive in that they are designed to address problems in a similar manner and with similar results as a human expert. The basic structure of an expert system is comprised of three functionally separate components: (a) knowledge base, which contains a representation of domain related facts; (b) means of knowledge base use to solve a problem, inference mechanism; and (c) working memory, which records the input data and progress for each problem. Given the complexity and cost of expert system construction, it is imperative that system developers and researchers attend to research issues which are critical to knowledge engineering. These questions can be categorized according to the parts of an expert system: (a) knowledge representation; (b) knowledge utilization; and (c) knowledge acquisition. A knowledge acquisition procedure is presented which displays the relationship between subject matter expert expertise consisting of declarative knowledge, procedural knowledge, heuristics, formal rules, and meta-rules. The knowledge engineer uses one or a combination of elicitation methods to gather relevant data to eventually build the components of an expert system. Further explained are the acquisition methods: (a) structured interview; (b) verbal reports; (c) teaching the subject matter; (d) observation; and (e) automated knowledge acquisition tools. The paper concludes with a discussion of the future research issues concerned with using knowledge mapping and task analysis vs. knowledge acquisition techniques.     

Integrating forward-chaining rules with operations and permanent knowledge bases

Traditionally, rule-based forward-chaining systems are considered to be standalone, working on a volatile memory. This paper focuses on the integration of forward-chaining rules with command-driven programming paradigms in the context of permanent, integrated knowledge bases. A system architecture is proposed that integrates the data management functions of large computerized knowledge bases into a module called a knowledge base management system (KBMS). Experiences we had in integrating rules with operations into a prototype KBMS called DALI are surveyed. For this integration, a new form of production rule, called the activation pattern controlled rule, is introduced, which augments traditional forward-chaining rules by a second, additional left-hand side, which allows making rules sensitive to calls of particular operations. Activation pattern controlled rules play an important role in DALI's system architecture, because they facilitate the storage of knowledge that has been specified relying on mixed programming, a combination of data-driven, command-driven, and preventive programming. The general problems of implementing permanent knowledge bases that contain rules and operations are discussed, and an algorithm for implementating activation pattern controlled rules, called IPTREAT, a generalization of the TREAT algorithm, is provided. Furthermore, the paper intends to clarify the differences between traditional, volatile rule-based systems and rule-based systems that are geared toward knowledge integration by supporting a permanent knowledge base.This paper is an extended and significantly revised version of a paper entitled Integrating Rules into a Knowledge Base Management System, which was presented at the First International Conference on Systems Integration, April 1990 [1].     

An expert system for setting time steps in dynamic finite element programs

An expert system, ETUDES—Expert Time integration control Using Deep and Surface Knowledge System, which addresses the determination of the timestep for time integration of linear structural dynamic equations is described. This time-step may also be applicable for a moderately nonlinear simulation of the same structure. The program also determines whether an explicit or implicit method is most efficient for the particular simulation. A production rule programming system written in OPS5 is used for the implementation of this prototype expert system. Issues relating to the expert system architecture for this application, such as knowledge representation and structure, as well as domain knowledge are discussed. The prototype is evaluated by measuring it's performance in various benchmark model problems.     

基于MAS的专家系统开发工具

结合分布式人工智能理论和面向对象及面向智能体技术，给出基于MAS（Multi0agent System)的专家系统开发工具的系统结构和设计，并描述了系统中的知识通信语言和协作机制。采用Java语言，在分布式网络环境下，实现了该工具的原型系统。用该工具开发了一个基于MAS的协作中医诊断专家系统，运行效果良好。