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.

Case-Based Reasoning (CBR) is a relatively recent problem solving technique that is attracting increasing attention. However, the number of people with first-hand theoretical or practical experience of CBR is still small. The main objective of this review is to provide a comprehensive overview of the subject to people new to CBR. The paper outlines the development of CBR in the US in the 1980s. It describes the fundamental techniques of CBR and contrasts its approach to that of model-based reasoning systems.1 A critical review of currently available CBR software tools is followed by descriptions of CBR applications both from academic research and, in more detail, three CBR systems that are presently being used commercially. Each of the three commercial case studies highlights features that made CBR particularly suitable for the application. Moreover, the last case study describes a development methodology for implementing CBR systems. The paper concludes with a research agenda for CBR. A detailed categorized bibliography of CBR research is provided in a companion paper (Marir & Watson, 1994).

The purpose of this paper is to review the use of knowledge-based systems and artificial intelligence (AI) in business. Part I of this paper provided a broad survey of the use of AI in business, summarizing the application of AI in a number of business domains. In addition, it also provided a summary of the use of different forms of knowledge representation in business applications. Part I has a large set of references, including a number of survey papers, focusing on AI in business. Part II of this paper consists of more detailed analysis of particular systems or issues affecting AI in business. It examines technical issues which are central to the construction of business AI systems, and it also examines the commercial contribution made by methods for the development of AI systems. In addition, part II looks at integration between AI and more traditional information systems. AI can be used to add value to many existing information systems, such as database management systems. Particular attention is given to the integration of AI with operations research, which is the one of the primary “competitors” of AI, providing an alternative set of support tools for decision making.

Business organizations are not concerned only with technology issues; there is also concern about the impact of AI on organizations. Further, the evaluation of AI often is based on an economic view of the world. Part II therefore investigates the organizational impact of AI, and the economics of AI, including issues such as value creation. The format of Part II is as follows: Section 8 analyses techniques for improving the performance of AI systems, thus maximizing economic return. Section 9 looks at different forms of uncertainty and ambiguity which must be dealt with by AI systems. It examines the contributions of fuzzy logic and numerical measures of certainty to handling these problems. Section 10 examines the usefulness of different approaches to knowledge acquisition in business situations, and investigates the benefits of methodological approaches to AI applications. It also looks at more recent AI programming techniques which eliminate the need for knowledge elicitation from an expert: neural networks, case-based reasoning and genetic algorithms are discussed. Sections 11 and 12 examine issues of integrating AI systems. Generally, the use of AI in business settings must ultimately be integrated with the broader base of corporate information systems. Section 11 looks at integration with information systems in general, and section 12 looks particularly at integration with operations research. Sections 13 and 14 review the organizational and economic impact of AI. Finally, section 15 provides a brief summary of part II.

In the field of knowledge engineering, dissatisfaction with the rapid-prototyping approach has led to a number of more principled methodologies for the contruction of knowledge-based systems. Instead of immediately implementing the gathered and interpreted knowledge in a given implementation formalism according to the rapid-prototyping approach, many such methodologies centre around the notion of a conceptual model: an abstract, implementation independent description of the relevant problem solving expertise. A conceptual model should describe the task which is solved by the system and the knowledge which is required by it. Although such conceptual models more precisely, and operationally as a means for model evaluation. In this paper, we study a number of such formal and operational languages for specifying conceptual models. To enable a meaningful comparison of such languages, we focus on languages which are all aimed at the same underlying conceptual model, namely that from the KADS method for building KBS. We describe eight formal languages for KADS models of expertise, and compare these languages with respect to their modelling primitives, their semantics, their implementations and their applications, Future research issues in the area of formal and operational specification languages for KBS are identified as the result of studying these languages. The paper also contains an extensive bibliography of research in this area.

Regulatory information has a direct and significant effect on the safety, economics and quality of many industries' operations and end-products. However, standards documents are often voluminous and can be complex. Users of such standards need to invest considerable time and effort to become familiar with the structure and content of the documents. Therefore, it has long been recognized that computer handling of this information is a potentially useful application area. This paper provides an overview of the work that has been done to provide computer systems that will help both users and authors of regulations. The emphasis is on using advanced symbolic computing techniques that would help in accessing, interpreting and applying the information.

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.

The inheritance problem can be simply stated: for any instantiation of an inheritance network, say a specific hierarchy Γ, find a conclusion set for Γ. In other words, find out what is logically entailed by Γ. This can be done in two ways: either by defining a deductive or proof theoretic definition to determine what paths are entailed by a network; or by translating the individual links in the network to a more general nonmonotonic logic and using its model and proof theory to generate entailments that correspond to what one would expect from “viewing” the inheritance hierarchy. Two approaches to a solution to the inheritance problem structure this paper. The first is widely known as the “path-based” or “proof theoretic”, and the second, the “Model-based” or “model theoretic”. The two approaches result in both a different interpretation of default links as well as a variation in the entailment strategy for a solution to teh inheritance problem. In either case, the entailments produced need some intuitive interpretation, which can be either credulous or skeptical. The semantics of both skeptical and credulous inheritance reasoners are examined.

Case-Based Reasoning (CBR) is a fresh reasoning paradigm for the design of expert systems in domains that may not be appropriate for other reasoning paradigms such as model-based reasoning. As a result of this, and because of its resemblance to human reasoning, CBR has attracted increasing interest both from those experienced in developing expert systems and from novices. Although CBR is a relatively new discipline, there are an increasing number of papers and books being published on the subject. In this context, this bibliographic categorization is an accompanying paper to a review of CBR by the same authors. The objective of this paper is to help researchers quickly identify relevant references.

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.

This paper reviews a number of hybrid Case-Based Reasoning (CBR) systems. These systems are hybrid because the CBR components cooperate with one or more “co-reasoners” which employ a different type of reasoning strategy (e.g. qualitative simulation, constraint satisfaction, etc.). In this paper, we propose that CBR is in fact an inherently hybrid process. We review a number of systems and identify three classes of architecture which have been used for hybrid systems. We believe that to successfully exploit a co-reasoner within a CBR system it is necessary to analyse where, when, why and how the information provided by the co-reasoner will be used. We propose the KADS methodology as a suitable way of performing such an analysis and illustrate its use by example. We conclude by considering the control issues associated with the construction of hybrid CBR systems. We review the requirements of such systems and consider how well the two existing cooperation architectures match those requirements.

As the number and size of very large databases continues to grow rapidly, so does the need to make sense of them. This need is addressed by the field called knowledge Discovery in Databases (KDD), which combines approaches from machine learning, statistics, intelligent databases, and knowledge acquisition. KDD encompasses a number of different discovery methods, such as clustering, data summarization, learning classification rules, finding dependency networks, analysing changes, and detecting anomalies (Matheus et at., 1993).

Recently there has been an increase in the number of computer aided design systems developed explicitly representing knowledge about the functionality of engineering designs. Reviewing these systems provides an understanding of the methods workers use to encapsulate knowledge of functionality within their systems. A number of issues are addressed to reveal the nature of their approaches. The developers' perception of functionality is discussed to identify variations in understanding of function and to establish the existence of any consensus. Methods of representing this knowledge are examined, thereby identifying representation types or combinations used and the advantages to be gained from any single representation. Illustrations of the manipulation of function shows how this type of knowledge can be used to support reasoning during early stage design. A survey of relationships with other design characteristics as a testimony to the manipulation of functionality is used to impact other aspects of a design. Through knowledge of relationships some models of the design process are posited by workers. A study of these bears evidence of the role of function in design and the stages at which its use is significant.

Case-based reasoning (CBR) systems reason from experience: they solve new problems by retrieving relevant prior cases and adapting them to fit new situations. In 1988 the first case-based reasoning workshop, sponsored by DARPA, identified theoretical foundations and fundamental issues for case-based reasoning research. Since then, much investigation has examined the CBR process itself, the validity of CBR as a cognitive model, and the application of CBR technology. The results of that work include refinements in theories of the case-based reasoning process, psychological evidence for human case-based reasoning, and the fielding of over 100 CBR applications.

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.

This paper provides a survey of the state of the art in plausible reasoning, that is exception tolerant reasoning under incomplete information. Three requirements are necessary for a formalism in order to cope with this problem: (i) making a clear distinction between factual information and generic knowledge; (ii) having a correct representation of partial ignorance; (iii) providing a nonmonotonic inference mechanism. Classical logic fails on requirements (i) and (iii), whilst the Bayesian approach does not fulfil (ii) in an unbiased way. In this perspective, various uncertainty modelling frameworks are reviewed: MYCIN-like fully compositional calculi, belief functions, upper and lower probability systems, and possibility theory. Possibility theory enables classical logic to be extended to layered sets of formulae, where layers express certainty levels. Finally, it is explained how generic knowledge can be expressed by constraints on possibility measures, and how possibilistic inferences can encode nonmonotonic reasoning in agreement with the Lehmann et al. postulates.

The Workshop on Formal Specification Methods for Knowledge-based Systems (KBS) took place in Amsterdam on August 8 1994 as part of the workshop program of the 11th European Conference on Artificial Intelligence (ECAI'94). It was the sixth workshop in a series concerned with the development and application of formal and executable specification languages for KBSs. Starting from the first familiarization workshop at GMD in Bonn 1992, where the different research groups met for the first time, further successor workshops were held at the University of Karlsruhe, the University of Amsterdam, and again at GMD in Bonn. Additionally, at ECAI'92 in Vienna, a workshop was held to compare different specification approaches for complex multi-layered KBSs.

The first conference on Uncertainty in Artificial Intelligence was held in 1985 by a group of people who felt that their views on the use of probability theory were not receiving a fair hearing from the rest of the Al community. At the time, mainstream opinion held that computational complexity of, and the amount of data required by, probabilistic methods made them inappropriate for realistic applications. As a result, those who claimed that probability theory was an adequate, if not the only adequate, method of handling uncertainty received a somewhat frosty reception.