This paper reviews many of the very varied concepts of uncertainty used in AI. Because of their great popularity and generality “parallel certainty inference” techniques, so-called, are prominently in the foreground. We illustrate and comment in detail on three of these techniques; Bayes' theory (section 2); Dempster-Shafer theory (section 3); Cohen's model of endorsements (section 4), and give an account of the debate that has arisen around each of them. Techniques of a different kind (such as Zadeh's fuzzy-sets, fuzzy-logic theory, and the use of non-standard logics and methods that manage uncertainty without explicitly dealing with it) may be seen in the background (section 5).

The discussion of technicalities is accompanied by a historical and philosophical excursion on the nature and the use of uncertainty (section 1), and by a brief discussion of the problem of choosing an adequate AI approach to the treatment of uncertainty (section 6). The aim of the paper is to highlight the complex nature of uncertainty and to argue for an open-minded attitude towards its representation and use. In this spirit the pros and cons of uncertainty treatment techniques are presented in order to reflect the various uncertainty types. A guide to the literature in the field, and an extensive bibliography are appended.

This paper proposes that managing uncertainty is a control problem, a task for the control component of AI systems that decides what to do next. This view emphasizes the process of planning and executing sequences of actions that simultaneously satisfy domain goals and minimize uncertainty. The paper reviews AI systems that manage uncertainty by control. It is not an exhaustive survey, but rather illustrates issues in managing uncertainty with selected AI programs.

For successful application of knowledge-based systems, “knowledge transfer” (KT) should be receiving more attention. Knowledge transfer is defined as a flow of knowledge from knowledge suppliers through knowledge-based systems to users. Insufficient state-of-the-application of knowledge-based systems in Japan and its near future directions are analysed in terms of KT. Some example tasks for improving KT are reported. These tasks were experienced by the author in the course of 10 years of research and development on knowledge-based systems for project management.

Knowledge engineering (KE) has now provided some effective techniques for formalization of knowledge about goals and actions. These techniques could open new areas of research to statisticians. Experimental systems designed to assist users of statistics have been constructed in experiment design, data analysis, technique application, and technique selection. Knowledge formalization has also been used in experimental programs to assist statisticians in doing data analysis and in building consultation systems. The best-explored application of KE techniques is building consultation systems. It is now a promising area for development. Analogies with successful artificial intelligence AI applications in other fields suggest other statistical applications worth exploring. Opening new areas to research and providing new tools to users would make considerable changes in the use and production of statistical techniques. However, applying currently available KE techniques will lead to more work for statisticians, not less.

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 circum-stances in which a strategy of cognitive emulation is useful.

A technology transfer programme, where people from industry have been educated and trained in knowledge engineering on a project basis, has been in operation since 1984 in the Computer and Information Science Department at Linköping University. This review presents the background for the programme, its organization, examples of training projects, educational activities and plans for the future development.

This review presents a brief survey of approaches to describing dialogues with computers which are relevant to improving communication between users and knowledge based systems. One approach to achieving improvements is to exploit the conventions of human communication. The review draws together some of the work on co-operative behavour which reflects these conventions. An analysis is presented which goes some way towards identifying the knowledge required and the issues to be tackled.

The traditions of image processing and knowledge engineering have developed separately. Work on AI vision systems lies between the two traditions but only recently has attention been given to combining practical imaging systems with methods for exploiting knowledge in interpreting the contents of an image. Five general approaches to combining knowledge based expert systems with imaging technologies are discussed. Particular attention is paid to the requirement for techniques which transform a pixel array into a symbolic form suitable for interpretation, and current obstacles to a general solution. Interpretation of biomedical images is particularly problematic because of statistical, structural and temporal variation in morphology of objects and structures. Some ways in which knowledge of shape, structure, and object classifications may contribute to this interpretation are discussed. The survey focuses on biomedical images but many of the issues are of general relevance to work in image understanding.

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.

Some of the issues involved in purchasing and using one of the powerful Knowledge Engineering Toolkits are examined. A case study problem is described and several products are reviewed as potential environments for implementing a solution. Experiences of using one System, ART, are described and observations are made regarded the selection, evaluation and use of such packages.

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.

The University of Edinburgh established the Artificial Institute (AIAI) in 1984 with the objective of transferring the technologies of artificial intelligence from the academic research environment to the practical worlds of commerce, government and industry.

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.

This paper describes some of our experience gathered during the development of an expert system, the press lineup advisor, in which we used the commercially available expert system development tool, S.1.™ Discussion includes: (1) how we used S. l to develop a system which solves a configuration problem; (2) difficulties we encountered when applying S.1 to this specific reasoning problem; (3) limitations of S.1 from both problem-solving and operational points of view and (4) issues remaining to be solved with respect to generalization of the system.

This article outlines the basic corporate engineering process that leads to the commercialization of a knowledge engineering concept, or software process. The development of a commercial product and enterprise from a technology-driven research effort or idea is quite similar to the process of continual refinement and hard work demanded by the scientific method, so the fundamentals of corporate and product development should not appear foreign to academics or those involved in corporate research. Knowledge engineering and related disciplines are on the verge of significant commercial product development efforts. The process of commercialization of ideas, particularly those derived from academic and research environments may not be intuitively obvious to the KE professional. The author outlines the general process of commercializing KE products and describes the three primary functional activities required to undertake a successful technology-driven commercial enterprise—market and customer definition, the development of the product prototype and corporate infrastructure, and capitalization.