As recently as a year ago, trade journal articles that discussed expert systems often carried a great deal of hyperbole and inflated claims. Many otherwise sophisticated technologists put expert-system techniques in the same category with such far-off ideas as orbiting missile defense systems and thinking machines.

Computer induction is discussed in the light of concerns recently expressed by Bloomfield about the use of such algorithms in expert systems construction.

Like other industrialized countries, France is currently enjoying a vogue for artificial intelligence and, generally, for hardware and software components and structures which will be needed for the design and implementation of the computer applications of the 1990s.

Since public announcement of MITI's Fifth Generation Project in October 1981, the French scientific and industrial communications have exhibited increasing enthusiasm for AI languages, expert systems, man-computer interaction, novel computer architectures, and knowledge-based computer systems as a whole. The choice of the Prolog language for the Japanese project has stimulated many French industrialists to be aware of the existence of a basic AI tool designed mainly in France.

In spite of the present fashion, often maintained by the journalistic milieu, it would be inaccurate to say that the French fifth generation project goes back to the Japanese announcement. The MITI project has certainly been a catalyst of ministerial and industrial awareness, but the bulk of ongoing projects stem from earlier work most often funded by government agencies.

In spite of the current thrust in AI and the centralizing habit in France, a “flagship” AI project cannot be identified. French Research and Development initiatives in artificial intelligence in general, and expert systems in particular, correspond more to a set of distinct projects. These frequently complement each other in technical scope and in their scientific and industrial objectives.

Knowledge-based System (KBS) technologies have been applied to a variety of knowledge-related tasks with varying degrees of success. Differentiating among classes of knowledge-related tasks, based on the amounts of problem-solving knowledge and case-specific data involved, can provide valuable insight into why this occurs. Based on this comparison, four classes of problems are described. One class, of data-intensive tasks, includes problem types that are difficult or impossible for humans to perform, yet may be solved in a cost-effective manner using currently accessible KBS technology. The characteristic features of problems in this class are given, together with an example of a successfully fielded knowledge-based system that solves a problem from this class.

In their paper “Some Comments on Rule Induction” Bratko and Michie address only three of the seven concerns that I put forward in my original article. Significantly, they have chosen to ignore the deeper and more important philosophical issues which I raised, while seeking to deflect my concerns by proclaiming their own privileged position of “being in the know” as experienced rule inductionists. This is a strategy well-known to philosophers and sociologists of science, unfortunately, it tends to stifle debate and avoids the serious consideration of constructive criticism. It is therefore important to respond to the comments made by Bratko and Michie and to re-focus attention on the central themes of my argument.

The main purpose of this survey was to update a survey report prepared for the UK Alvey Directorate during 1984. The update was focused on business, and had to be qualitative because the increased confidentiality associated with the majority of projects precluded a quantitative analysis, and is confined to a synthesis of the impressions we have obtained. The survey was not intended to be representative. On the contrary, organizations were identified as being amongst those most likely to provide interesting application experience. This led to an arbitrary selection. For example, all banks were excluded because it was felt they were not yet ready to talk of their experiences. The companies approached consisted of five organizations visited in 1983, five further user companies and five service companies (e.g. consultants and software houses).

This paper gives the personal view of a Japanese knowledge engineer, concerning the present status of the evolution of knowledge engineering in Japan, especially its applications in the business field, user evaluation and acceptance, problems, and future prospects.

There is a growing awareness of the potential of expert systems in the accounting profession, although the number of applications is limited. This paper assesses whether the profession is dragging its heels, or whether it is being wisely cautious.

Solving a legal problem is a complex business. First, the lawyer—or whoever is doing the solving—has to assemble and marshall the facts of his case in as orderly a fashion as his knowledge of the relevant law will allow. Secondly, he must confront the range of pertinent legal sources and decompose these into manageable and applicable units. Thirdly, he will subsume the particular facts of his case under the more general terms of his decomposed units. And, finally, he draws inferences about the applicable legal provisions and their impact on the facts. Of course, assembly, decomposition, subsumption and inference may not occur in linear progression. What is more, problems can cccur at each stage and interim evaluations and judgments must be made.