Progress in software engineering has led to system development following models of the system life cycle. These models incorporate the use of prototyping and formal methods of program verification. They are becoming supported by integrated project support environments and permit the planning and monitoring of software development projects.

In contrast, knowledge based systems (KBS) are developed using informal views of the system life cycle. Tools have been developed to support some stages of the life cycle in an undisciplined manner. The commercial use of KBS needs development projects to be planned and monitored. This requires methods and tools based on systematic life cycle models to be established for KBS.

This paper reviews the current state of life cycle approaches to software engineering and KBS development projects in order to provide a direction for the development of methodical KBS life cycle models.

First-generation expert systems have significant limitations, often attributed to their not being sufficiently deep. However, a generally accepted answer to “What is a deep expert system?” is still to be given. To answer this question one needs to answer “Why do first-generation systems exhibit the limitations they do?” thus identifying what is missing from first-generation systems and therefore setting the design objectives for second-generation (i.e. deep) systems. Several second-generation architectures have been proposed; inherent in each of these architectures is a definition of deepness. Some of the proposed architectures have been designed with the objective of alleviating a subset, rather than the whole set, of the first-generation limitations. Such approaches are prone to local, non-robust solutions. In this paper we analyze the limitations (under the categories: human-computer interaction, problem-solving flexibility, and extensibility) of the first-generation expert systems thus setting design goals for second-generation systems. On the basis of this analysis proposed second-generation architectures are reviewed and compared. The paper concludes by presenting requirements for a generic second-generation architecture.

Recent approaches to providing advisory knowledge-based systems with explanation capabilities are reviewed. The importance of explaining a system's behaviour and conclusions was recognized early in the development of expert systems. Initial approaches were based on the presentation of an edited proof trace to the user, but while helpful for debugging knowledge bases, these explanations are of limited value to most users. Current work aims to expand the kinds of explanation which can be offered and to embed explanations into a dialogue so that the topic of the explanation can be negotiated between the user and the system. This raises issues of mutual knowledge and dialogue control which are discussed in the review.