To summarize, Software engineering Environments provides a useful overview of recent progress in software engineering tools and methods; particularly from a UK perspective. The majority of the contributions are well-written but the general paucity of introductory material and the level of technical detail in some chapters might prove daunting to readers unfamiliar with software engineering. It would have been helpful if the editor had made more effort to draw out the common themes and important issues; either by linking sections of the book with short discussion (as Hawley did for Artificial Intelligence Programming Environments) which helps to smooth over the changes in prose style or by cross-referencing points made by different authors. As it stands the result is rather a peculiar hybrid between a conference proceedings and a multi-author text book.

Intelligent tutoring systems can be seen as a next step for computer-based training systems, but also as an important by-product of knowledge-based expert systems. This paper surveys the development and progress in the area, with a special emphasis on the potential for an emerging engineering discipline as opposed to a mere crafting of systems. Major components in intelligent tutoring systems as realized so far are discussed, and key issues for successful future development identified. Knowledge representation, student modelling, planning, natural language issues, explanations and learning are discussed in more depth as being the cornerstones of both tutoring systems and artificial intelligence. Examples from specific implementations are used to illustrate key points.In the concluding discussion we present our attempt at dealing with some of the problems facing the area. In the project Knowledge-Linker, we aim at extending the functionality of a knowledge-based system with tutoring capabilities, and suggest one way of explicitly dealing with teaching strategies.

The claim is frequently made that human judgement and reasoning are vulnerable to cognitive biases. Such biases are assumed to be inherent in that they are attributed to the nature of the mental processes that produce judgement. In this paper, we review the psychological evidence for this claim in the context of the debate concerning human judgemental competence under uncertainty. We consider recent counter-arguments which suggest that the evidence for cognitive biases may be dependent on observations of performance on inappropriate tasks and by comparisons with inappropriate normative standards. We also consider the practical implications for the design of decision support systems.

When a mechanic troubleshoots a car, a physician narrows down a diagnosis, a designer selects from among options, or a corporation conducts a post-mortem of a venture gone wrong, the nature knowledge used is more likely than not, the same. That knowledge is “function”, though in the above scenarios it may be variously referred to as “responsibility”, “causal factor” or “missing element”. Reasoning about function is an integral part of human reasoning. It is currently attracting a lot of attention in artificial intelligence.

Constraint logic programming (CLP) is a generalization of logic programming (LP) where unification, the basic operation of LP languages, is replaced by constraint handling in a constraint system. The resulting languages combine the advantages of LP (declarative semantics, nondeterminism, relational form) with the efficiency of constraint-solving algorithms. For some classes of combinatorial search problems, they shorten the development time significantly while preserving most of the efficiency of imperative languages.This paper surveys this new class of programming languages from their underlying theory, to their constraint systems, and to their applications to combinatorial problems.

This paper explores the implications of research results in behavioural decision theory on knowledge engineering. Behavioural decision theory, with its performance (versus process) orientation, can tell us a great deal about the validity of human expert knowledge, and when it should be modelled. A brief history of behavioural decision theory is provided. Implications for knowledge elicitation and representation are discussed. An approach to knowledge engineering is proposed that takes into account these implications.

This paper discusses the development of Information Presentation Systems (IPS). It identifies that these systems have had two development paths; generic and application-specific. It discusses the main pieces of early work, and identifies the significant problems and shortcomings associated with them. The development of later systems is traced through to the present day, and outstanding research issues are identified.