Diagnosis was among the first subjects investigated when digital computers became available. It still remains an important research area, in which several new developments have taken place in the last decade. One of these new developments is the use of detailed domain models in knowledge-based systems for the purpose of diagnosis, often referred to as “model-based” diagnosis. Typically, such models embody knowledge of the normal or abnormal structure and behaviour of the modelled objects in a domain. Models of the structure and workings of technical devices, and causal models of disease processes in medicine are two examples. In this article, the most important notions of diagnosis and their formalisation are reviewed and brought in perspec tive. In addition, attention is focused on a number of general frameworks of diagnosis, which offer sufficient flexibility for expressing several types of diagnosis.

Belief revision is the process of rearranging a knowledge base to preserve global consistency whilst accommodating incoming information. Early approaches to belief revision used symbolic model theoretic methods, considering the problem as one of changi ng a logical theory. More recent approaches have adopted qualitative syntactic methods, taking them into the area of truth maintenance systems, and numerical mathematical methods, thus moving into the mainstream literature of uncertainty management. Multi -agent systems, in which information may come from a variety of human and artificial sources with different degrees of reliability, seem to be a natural domain for belief revision. The aim of this paper is to give a synoptic perspective of this composite subject from the clear air of the high theoretical peaks down to the muddy plain of practical algorithms.

Research in knowledge-based systems has shown that the use of multiple knowledge representation formalisms and reasoning mechanisms for achieving a specific task in complex domains could result in efficient and effective problem solving. This has led t o the development of a number of general architectures and application-specific systems integrating multiple problem solvers. A problem solver is defined to be an association between a knowledge intensive (sub)task, an inference mechanism and a knowledge representation formalism on which the inference mechanism is working to achieve the (sub)task. A knowledge-based system making use of different problem solvers should address a number of critical aspects of integration of the solvers, like interaction, in vocation, reactiveness, learning and expandability. The aim of this paper is to distinguish and discuss essential integration aspects, and to review a number of proposed general hybrid architectures and application specific hybrid systems on the basis of these aspects. The review shows that none of these general architectures or application specific systems directly addresses all the identified integration aspects. In general, the limitations exhibited by these systems are due to the naive form of interac tion and invocation of the integrated solvers. We give a high level specification for a competent hybrid knowledge-based architecture that supports the identified integration aspects.

The KEML (Knowledge Engineering Methods and Languages) workshop which took place on 22–24 January at the Open University in Milton Keynes (UK) was the seventh in a series of workshops on methods and languages for knowledge engineering. Although t he KEML acronym suggests a broad knowledge engineering connotation, in practice the main emphasis of these workshops is on the construction, formalisation, verification and use of knowledge models. The term “knowledge model” originate s from the work of Allen Newell (1982), who proposed a level of description — the knowledge level — which abstracts from implementation-related considerations to focus on the actions, goals and knowledge embodied by a problem solving agent.

Intelligent multimedia systems (IMMS) assist users in choosing the information content, and to design the information presentation, that satisfies their needs and idiosyncrasies. Towards this target, IMMSs posses and utilize knowledge concerning the means and function (purpose) of a presentation. ‘Means’ denote communication techniques (information carriers and modalities), as well as information content and information characteristics. The ‘function of a pr esentation’ denotes the communicative purpose of the presentation, methods for achieving that purpose, as well as a user's goals and tasks.