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doi:10.1016/S0957-4174(02)00149-5    
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Copyright © 2002 Elsevier Science Ltd. All rights reserved.

What makes expert systems survive over 10 years—empirical evaluation of several engineering applications

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Jukka K. NurminenCorresponding Author Contact Information, E-mail The Corresponding Author, Olli Karonen and Kimmo Hätönen

Nokia Research Center, P.O. Box 407, FIN-00045 Nokia Group, Finland


Available online 22 October 2002.

Abstract

This case study analyzes eight expert system applications that have successfully been in industrial use for a long time. We have personally been involved in the development of these applications and are thus in a good position to analyze what is important for a successful application and what kind of mistakes can be made. Since the development of the applications started in 1986–1990 and some of them are still in use we are able to observe what has happened to those applications during their lifetime. Our key observations are related to the scope of the applications, to the trade-off between usability and automation, to the role of human experts in the use and development of expert systems, on the technical solutions used, on aspects of the operation of the expert system and on the similarities between expert systems and information systems. The key findings are expressed as 20 hypotheses for successful expert systems. The support of each application to the hypotheses is discussed.

Author Keywords: Expert systems; Industrial applications; Lessons learned; Success factors; Implementation issues

Article Outline

1. Introduction
2. Applications
2.1. NICAD
2.2. RFT
2.3. Nokia planner
2.4. CabCon
2.5. DXCON
2.6. DMG
2.7. DXLib
2.8. MATIAS
3. Characteristics of successful systems
3.1. Domain
3.1.1. Narrow scope
3.1.2. Focused objective
3.1.3. Stability of environment
3.1.4. High degree of automation
3.1.5. High degree of repetition
3.1.6. Small project
3.2. Development
3.2.1. Users prefer usability over automation
3.2.2. Expert systems complement rather than replace human experts
3.2.3. Early benefits to experts themselves are important
3.2.4. AI should be embedded part of a bigger system
3.2.5. Simple, straightforward solutions work best
3.2.6. If-then rules considered harmful
3.2.7. Lots of custom work
3.2.8. Fast and agile development is important
3.2.9. Knowledge-based applications are based on domain specific application engines and generators
3.2.10. Monolithic applications are sometimes better than knowledge-based applications
3.2.11. Rules of normal SW development apply
3.3. Operation
3.3.1. Move towards mainstream software and hardware platforms
3.3.2. Successful systems tend to move out from the company
3.4. General
3.4.1. The difference between expert systems and information systems is small
4. Conclusions
References


Corresponding Author Contact Information Corresponding author. Tel.: +358-7180-36442; fax: +358-7180-36855


 
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