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).

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.

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.

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.