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.

TFL, the Task Formal Language, has been developed for integrating the static and dynamic aspects of knowledge based systems. This paper focuses on the formal specification of dynamic behaviour. Although fundamental in knowledge based systems, strategic reasoning has been rather neglected until now by the existing formal specifications. Most languages were generally more focused on the domain and problem-solving knowledge specification than on the control. The formalisation presented here differs from previous ones in several aspects. First, a different representation of dynamic knowledge is proposed: TFL is based on Algebraic Data Types, as opposed to dynamic or temporal logic. Second, dynamic strategic reasoning is emphasised, whereas existing languages only offer to specify algorithmic control. Then, TFL does not only provide the specification of the problem-solving knowledge of the object system, but also of its strategic knowledge. Finally, the dynamic knowledge of the meta-system itself is also specified. Moreover, modularisation is another important feature of the presented language.

In his thought-provoking paper, Valdes-Perez (1996, this volume) carefully describes the methodology and future directions for research in Machine Scientific Discovery (MSD), as viewed predominantly from the standpoint of computer science. Here, I shall offer some remarks from the angle of domain sciences (despite the fact that my basic area of expertise is actually general and computational linguistics).

In this paper we consider the role case-based reasoning has had in development of computer-aided instruction systems. We survey several case-based teaching systems, each of which is representative of a basic pedagogical principle that motivated its development. Firstly, 15 pedagogical principles are presented that were identified from the analysis of case-based teaching systems. We present some background to the principles, and indicate which systems they are incorporated in. Next, the teaching systems themselves are described, with emphasis on how case-based reasoning has been applied. Finally, we discuss some problems to be addressed when case-based planning is applied to lesson planning within a tutoring system.

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.

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.

Time is one of the most relevant topics in AI. It plays a major role in several of AI research areas, ranging from logical foundations to applications of knowledge-based systems. Despite the ubiquity of time in AI, researchers tend to specialise and focus on time in particular contexts or applications, overlooking meaningful connections between different areas. In an attempt to promote crossfertilisation and reduce isolation, the Temporal Representation and Reasoning (TIME) workshop series was started in 1994. The third edition of the workshop was held on May 19–20 1996 in Key West, FL, with S. D. Goodwin and H. J. Hamilton as General Chairs, and L. Chittaro and A. Montanari as Program Chairs. A particular emphasis was given to the foundational aspects of temporal representation and reasoning through an investigation of the relationships between different approaches to temporal issues in AI, computer science and logic.