This paper presents an overview of fuzzy set theory and its application to the analysis and design of fuzzy expert control systems. Starting with a short account of the basic concepts and properties of fuzzy sets and fuzzy reasoning, a few fuzzy rule-based controllers, viz, basic single-input singleoutput fuzzy control, self-organizing fuzzy control, fuzzy PID supervisor, and the fuzzy PID incremental controller, are described in some detail. Then a survey of the theoretical results and applications is provided which gives a good picture of the current status of the field. This survey includes the work on neuro-fuzzy systems, and software systems for the representation and processing of fuzzy information. The paper closes with four application examples which show the type of results that must be expected from fuzzy expert control.

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.

Over the last ten years, molecular biologists and computer scientists have experimented with various artificial intelligence techniques, notably knowledge based and expert systems, qualitative simulation, natural language processing and various machine learning techniques. These techniques have been applied to problems in molecular data analysis, construction of advanced databases and modelling of biological systems. Practical results are now being obtained, notably in the representation and recognition of genetically significant structures, the assembly of genetic maps and prediction of the structure of complex molecules such as proteins. The paper outlines the principal methods used, surveys the findings to date, and identifies promising trends and current limitations.

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.