The expert systems business has been through a terrible period in 1987–88. In the USA, the “Gang of Four”—the new venture software companies which once led the industry—have all run into losses and cut back staff more or less severely. Symbolics, whose high priced Lisp workstations have made it the biggest single company in artificial intelligence (AI), has hit an equally dramatic reversal of fortunes.

The level of abstraction of much of the work in knowledge-based systems (the rule, frame, logic level) is too low to provide a rich enough vocabulary for knowledge and control. I provide an overview of a framework called the Generic Task approach that proposes that knowledge systems should be built out of building blocks, each of which is appropriate for a basic type of problem solving. Each generic task uses forms of knowledge and control strategies that are characteristic to it, and are in general conceptually closer to domain knowledge. This facilitates knowledge acquisition and can produce a more perspicuous explanation of problem solving. The relationship of the constructs at the generic task level to the rule-frame level is analogous to that between high-level programming languages and assembly languages in computer science. I describe a set of generic tasks that have been found particularly useful in constructing diagnostic, design and planning systems. In particular, I describe two tools, CSRL and DSPL, that are useful for building classification-based diagnostic systems and skeletal planning systems respectively, and a high level toolbox that is under construction called the Generic Task toolbox.

I thank the commentators for their time, and generally positive remarks on the promise of the task-specific approach, in particular the generic task (GT) proposal.

Johnson and Zualkernan would like a methodology for mapping domain knowledge onto one or more generic tasks so as to solve problems efficiently. This stage of problem and domain analysis in which the kind of reasoning that goes on needs to be analysed in a vocabulary of generic tasks is very important, and in our laboratory we identify this as the epistemic analysis stage. For specific classes of problems we have developed guidelines on how to perform this mapping. For example, Bylander and Smith (Bylander and Smith, 1985) describe a set of criteria and guidelines for mapping medical knowledge into CSRL-like structures for diagnostic reasoning. Similarly Brown (1984) describes criteria for mapping design knowledge into DSPL-like structures.

Literature relevant to the design and development of graphical interfaces for knowledge-based systems is briefly reviewed and discussed. The efficiency of human-computer interaction depends to a large extent on the degree to which the human-machine interface can answer the user's cognitive needs and accurately support his or her natural cognitive processes and structures. Graphical interfaces can often be particularly suitable in this respect, especially in cases where the user's “natural idiom” is graphical. Illustrated examples are given of the way in which graphical interfaces have successfully been used in various fields with particular emphasis on their use in the field of knowledge-based systems. The paper ends with a brief discussion of possible future developments in the field of knowledge-based system interfaces and of the role that graphics might play in such developments.