Reuse of designs is a topic which is receiving growing attention from the research community. Reuse of designs addresses the reuse of former experience in new design situations; relevant existing cases are retrieved and adapted to new situations. This reuse is common practice for engineers working in practice, both for efficiency reasons and to prevent making the same mistakes over and over again.

The theme of this paper is to review work carried out in the area of applying expert systems and artificial intelligence in electrical power engineering. It surveys expert systems and artificial-intelligence-based algorithms developed for solving decision problems in power network and generator fault diagnosis, reactive power and voltage control, restoration of power supply, determination of load circuits for shedding in under-frequency load shedding schemes, generator scheduling, allocation of loads and circuits, load-flow planning, generation expansion planning and distribution expansion planning.

It is well known that a program—to accomplish any task—is welcomed by new users as long as it is easy to use, to understand, to learn. In the recent past, programs were written first and a nice adhoc interface was added later. The look and feel of this interface gradually became the convincing part of the whole system (program plus interface): in fact the way in which the screen presented the available commands, the possibility of on-line help, of undo operations, etc. were firstly greeted and then requested by the new users of complex programs.

This paper gives a state-of-the-art overview of the rapidly expanding field of user modelling in artificially intelligent systems. After showing how a user modelling component can improve parts of the processing of, and the interaction with, a large number of systems, the current situation in this field is sketched by means of a number of short descriptions of systems employing user modelling techniques. Next, a synthesis of this review is made by discussing the aspects on which the existing methods differ. It turns out that these aspects can best be approached from two points of view: the technical level and a more abstract, functional level. This dichotomy results in eight dimensions on which to compare the modelling methods. The synthesis is then completed by describing the existing modelling techniques, and classifying the reviewed systems. In the final section, current trends in the field are outlined and prerequisites for acceptance of user modelling on a larger scale are discussed.

Expert systems technology has been around for a long time, becoming increasingly easy to use, inexpensive and reliable in recent years. It would seem to provide an ideal vehicle for the dissemination of expertise in developing countries, particularly in the field of medicine, which was the focus of much early work in diagnostic systems. Despite the apparent match of a real problem and a credible solution, however, remarkably few AI systems for medicine in developing countries have been researched, designed and implemented. This paper addresses why this might be the case, reviews some of the extant systems and explores some of the design issues. Particular emphasis is placed on the question of “Appropriate Technology”. Various criteria for Appropriate Technology are explored, and an optimal set used to guide principles of design. It is argued that medical AI systems can satisfy these criteria, provided that sufficient care is taken in their design for the country of application.

It is often claimed that it is easy to write expert systems. This claim was examined by monitoring experienced programmers learning to use the S.I knowledge engineering tool. Their achievements and difficulties were examined using a framework that has emerged from previous research into novices learning to use standard programming languages. Even though the experienced programmers all had several years' experience of programming in more than one standard language, there were similarities between their difficulties in learning to use S.I and the difficulties of complete novices learning to program in standard languages.

The experienced programmers were however able to overcome their initial difficulties fairly quickly, but it is argued that complete novices would not find it so easy to do so. Also the experienced programmers did take time to develop a repertoire of schemeta for representing different kinds of factual, judgemental and procedural knowledge. It was concluded that in S.1, as with other programming languages and softwares tools, it is easy to learn how to do simple things, but difficult, even for experienced programmers to learn how to do more complex things.

No criticism of S.1 is implied. S.1 was found to be a suitable vehicle for introducing non-trivial knowledge engineering concepts, and we believe that similar difficulties would occur in learning to use other knowledge engineering tools.