The techniques of qualitative reasoning are now becoming sufficiently mature to be applied to real world problems. In order to better understand which techniques are being used successfully for real world applications, and which application areas can be suitably addressed using qualitative reasoning techniques, it is helpful to have a summary of what application oriented work has been done to date. This helps to provide a picture of the application areas in which the techniques are being applied, and who is working in each application domain. In this paper, we summarize over 40 relevant projects.

In the coming years, scheduling will be an increasingly important activity in the manufacturing and aerospace industries, especially at the US National Aeronautics and Space Administration (NASA). Expert systems have successfully been used to aid in the scheduling function. This paper surveys some of the conflict resolution approaches used in building expert scheduling systems, and then examines the feasibility of using an expert systems approach for scheduling activities and resolving conflicts for experimenters to use NASA Goddard-supported satellites. Recommendations for further reading in expert scheduling systems are given.

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.

Abstract interpretation is a principled approach to inferring properties of a program's execution by simulating that execution using an interpreter which computes over some abstraction of the program's usual, concrete domain, and which collects the information of interest during the execution. Abstract interpretation has been used as the basis of research in logic and functional programming, particularly in applications concerned with compiler optimizations. However, abstract interpretation has the potential to be used in other applications, such as debugging or verification of programs. In this paper we review the use of abstract interpretation to both compiler optimizations and to other applications, attempting to give a flavour of the kind of information it is possible to infer and some of the issues involved

The application area of knowledge-based expert systems is currently providing the main stimulus for developing powerful, parallel computer architectures. Languages for programming knowledge-based applications divide into four broad classes: Functional languages (e.g. LISP), Logic languages (e.g. PROLOG), Rule-Based languages (e.g. OPS5), and, what we refer to as self-organizing networks (e.g. BOLTZMANN machines).

Despite their many differences, a common problem for all language classes and their supporting machine architectures is parallelism: how to de-compose a single computation into a number of parallel tasks that can be distributed across an ensemble of processors. The aim of this paper is to review the four types of language for programming knowledge-based expert systems, and their supporting parallel machine architectures. In doing so we analyze the concepts and relationships that exist between the programming languages and their parallel machine architectures in terms of their strengths and limitations for exploiting parallelization.