Although expert systems and knowledge engineering are attracting great attention there is still confusion about what they are. Most expert systems deal with problems which are familiar to other disciplines and their distinctive character is not always recognised. This tutorial describes the origins of expert systems in Artificial Intelligence, and the technical concept of “knowledge”. The central feature is the ideal of explicitly representing knowledge as facts, rules and other symbolic structures, rather than the traditional representation as abstract numbers or algorithms. These developments yield new solutions to old problems, and ways of solving problems whcih have previously been thought to be the preserve of the human intellect.

The relentless growth of the computer industry over more than 30 years has been driven on by a series of major innovations. High-level languages; solid-state logic; compatible machine ranges; disk storage; time-sharing; data communications; virtual machine architectures; the use of LSI and solid-state memory; text processing; personal computers; sucessful packaged software; each advance in its turn has opened up new markets and set off a new spurt of expansion. A vital conclusion of this study is that expert systems promise to be another such major innovation.

Planning systems have been an active research topic within Artificial Intelligence for over two decades. There have been a number of techniques developed during that period which still form an essential part of many of today's planners. This paper introduces the techniques, attempts to classify some of the important research themes in AI planning and describes their historical development.

Shells and high-level programming language environments suffer from a number of shortcomings as knowledge engineering tools. We conclude that a variety of knowledge representation formalisms and a variety of controls regimes are needed. In addition guidelines should be provided about when to choose which knowledge representation formalism and which control regime. The guidelines should be based on properties of the task and the domain of the expert system. In order to arrive at these guidelines we first critically review some of the classifications of expert systems in the literature. We then give our own list of criteria. We test this list applying our criteria to a number of existing expert systems. As a caveat, we have not yet made a systematic attempt at correlating the criteria and different knowledge representations formalisms and control regimes, although we make some preliminary remarks throughout the paper.

Statistical expert systems are attracting increasing attention as a possible way to alleviate the shortage of expert consultant statisticians. This paper summarises the requirements of such systems, showing how the demands of data analysis are different from those of other fields, and describes some recent work.

The computer industry is in the early stages of a revolution. Its capability for processing natural languages will advance dramatically over the next few years. As a result, computer systems which deal with human language will begin to play a central role in the industry. Computers will becooome significantly more accessible to many people, and many tasks which are wholly manual will be handled electronically.

This paper aims to describe the current state of knowledge systems technology and its commercialisation in the US. First, knowledge systems are defined and placed in a historical context. The introduction is concluded with a preview of major ideas. The paper will assess the technological state of the art and will survey the current state of commercialisation. Finally, some anticipated future trends will be discussed.

The purpose of this paper is to evaluate and compare three of the most powerful expert system tools available: KEE from Intellicorp, Knowledge Craft from The Carnegie Group Inc., and ART from Inference Corporation.

These three tools are industrial development environments which are fully supported and well beyond research prototypes. They were implemented on Lisp machines initially, but will soon be available on conventional computers. The three systems are very flexible and offer many ways of representing knowledge.

The first part of the paper is a technical overview of each tool. The second part presents their common features. The third part discusses the advantages and drawbacks of each, according to types of application.

This work has been partially sponsored by the Commission of the European communities within the Esprit Project 932 entitled ‘Knowledged-based Real-Time supervision in Computer Integrated Manufacturing’. We are grateful to Didier Gaget-Soufflot (Graphael, France) and Bernard Raust (CGE/TITN) for their participation in the evaluation of KEE and ART respectivily.

The Japanese Fifth Generation Computer Systems (FGCS) project has chosen logic programming for its core programming language. It has recognized the major contribution that logic programming has to make not only in artificial intelligence but in database systems and software specification as well. It has recognized and intends to exploit the greater potential that logic programming has to offer for taking advantage of the parallelism possible with innovative multiprocessor computer architectures.

Cognitive emulation is an expert system design strategy which attempts to model system performance on human (expert) thinking. Arguments for and against cognitive emulation are reviewed. A major conclusion is that a significant degree of cognitive emulation is an inherent feature of design, but that an unselective application of the strategy is both unrealistic and undesirable. Pragmatic considerations which limit or facilitate the viability of a cognitive emulation approach are discussed. Particular attention is given to the conflict between cognitive emulation and established knowledge engineering objectives, detailed over 12 typical expert system features. The paper suggests circumstances in which a strategy of cognitive emulation is useful.

This paper examines the claim that machine induction can alleviate the current knowledge engineering bottleneck in expert system construction. It presents a case study of the rule induction software tool known as Expert-Ease and proposes a set of criteria which might guide the selection of appropriate domains.

The concept of an expert system covers an increasingly large group of software packages which often have more dissimilarities than points in common. We shall not attempt to give a precise definition of an expert system here, because this might impose too restrictive a framework on the rest of our discussion. We shall simply state that, as is generally recognized, an expert system is a piece of software intended to resolve a certain category of problems, that it uses for this purpose a large quantity of knowledge specific to the field in question, and that in each expert system there is a very distinct separation between this knowledge and the procedures which make use of it.