The client‐centred approach: expert system maintenance

Abstract

Developing an expert system is a considerable investment in time and money and yet very little attention has focused on keeping expert systems in regular beneficial use. The problems of maintaining expert systems and the lack of research that this area has attracted are major contributing factors to the continued lack of acceptance of these systems. This paper discusses the maintenance of expert systems within the context of the Client Centred Approach. Although there are many similarities between maintaining expert systems and conventional systems maintenance there is one vital differenceknowledge changes. Consequently an expert system requires regular evolutionary maintenance if its peqormance is not to be impaired. This paper is intended to promote discussion of maintenance issues within the community. We review some principles and techniques that can assist the maintenance of expert systems and describe some formative work towards a maintenance methodology derivedfrom a case study of maintaining a commercially-available expert system.

The Application of Product Variant Design Based on Case-Based Design in the Development of Stone Equipments

“…Specially, the application of expert system in CAD (Computer Aided Design) assuredly promotes the development of intelligent CAD. But these application systems inevitably result in such questions as follows [6]: question of knowledge acquirement, question of knowledge denotation, mistake in evolution ability, complex process of actualizing expert system, the requirement of special technique and difficulty to maintain in the process of system running, etc. Moreover, the CBD technique can avoid these questions.…”

“…(5) Referring to the domain knowledge, this system should rationally mend the selected template to adapt the new requirement. (6) In every design level, the system should devise accurate evaluation to the design result.…”

Theories and Methods for the Emergency Rescue System

“…But whether the knowledge is shallow or deep an explicit model of the domain must still be elicited and implemented often in the form of rules or perhaps more recently as object models. However, knowledge elicitation is a difficult process, often being referred to as the knowledge elicitation bottleneck; implementing KBS is a difficult and slow process requiring special skills; and once implemented they are difficult to maintain [2][3][4][5][6].…”

Medical decision support systems based on machine learning

“…The process of transforming human knowledge to machine-usable form is called knowledge acquisition and is considered a bottleneck [41] because it is time-and labor-intensive. In addition, maintaining the knowledge base is very labor-intensive [100,25].…”