Web-Based Expert Systems

Web-Based Expert Systems

Yanqing Duan (University of Luton, UK)
Copyright: © 2009 |Pages: 14
DOI: 10.4018/978-1-60566-026-4.ch656
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Abstract

Convergence of technologies in the Internet and the field of expert systems (ES) offer new ways of sharing and distributing knowledge (Sedbrook, 1998). Power (2000) argues that rapid advances in Internet technologies have opened new opportunities for enhancing traditional decision support systems and expert systems. Internet technology can change the way that an expert system is developed and distributed. For the first time, knowledge on any subject can directly be delivered to users anywhere and anytime through a Web-based ES. Because the main function of an ES is to mimic expertise and distribute expert knowledge to nonexperts, these benefits can be greatly enhanced with the emergence of the Internet. The current focus on networked and Internet-based applications demands new architectures for “intelligent” systems as well as creating new possibilities for research and development in this field (Caldwell, Breton, & Holburn, 2003). This article provides an overview of Web-based expert systems with examples. Benefits and challenges are discussed by comparing Web-based ES with traditional standalone ES from both the development and the application perspectives using Turban and Aronson’s knowledge engineering framework.
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Background

An expert system is “a system that uses human knowledge captured in a computer to solve problems that ordinarily require human expertise” (Turban & Aronson, 1998, p. 440). Durkin (1996) reports that many organisations have leveraged ES to increase productivity and profits through better business decisions. Although there have been reports of ES failures (Wong, 1996), research (Yoon, Guimaraes, & O’Neal, 1995) shows that many companies have remained enthusiastic proponents of the technology and continue to develop important ES applications.

The early applications of ES were standalone applications based on mainframe, Artificial Intelligence (AI) workstation or PC platforms. Later came local area network (LAN)-based distributed applications. Grove (2000) identified several problems associated with traditional ES applications, including knowledge bottleneck, performance brittleness, availability of the system, problems with individual software installation and upgrading, and a lack of common protocols for knowledge transfer.

Key Terms in this Chapter

Decision Support Systems (DSS): An interactive computer-based information system to support decision-making activities by utilising databases and model bases.

Expert Systems (ES): A computer-based system that uses human knowledge captured in a computer to solve problems that ordinarily require human expertise.

Inference Engine: A computer program that tries to derive answers from a knowledge base.

Web-Based Expert Systems: An expert system developed and distributed using Internet technologies.

Knowledge-Based Systems (KBS): A computer system that is programmed to imitate human problem-solving by means of artificial intelligence. Its core components are the knowledge base and the inference mechanisms. An expert system can be considered as one type of KBS.

Knowledge Engineering: An entire process of developing and maintaining artificial intelligent systems. The major activities in the process include knowledge acquisition, validation, representation, inferencing, explanation, and maintenance.

Artificial Intelligence (AI): The study on human intelligence and on how to simulate human intelligence via machines, such as computers.

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