Induced decision trees are an extensively-researched solution to classification tasks. For many practical tasks, the trees produced by tree-generation algorithms are not comprehensible to users due to their size and complexity. Although many tree induction algorithms have been shown to produce simpler, more comprehensible trees (or data structures derived from trees) with good classification accuracy, tree simplification has usually been of secondary concern relative to accuracy, and no attempt has been made to survey the literature from the perspective of simplification. We present a framework that organizes the approaches to tree simplification and summarize and critique the approaches within this framework. The purpose of this survey is to provide researchers and practitioners with a concise overview of tree-simplification approaches and insight into their relative capabilities. In our final discussion, we briefly describe some empirical findings and discuss the application of tree induction algorithms to case retrieval in case-based reasoning systems.

The main components of case-based reasoning are case retrieval and case reuse. While case retrieval mostly uses attribute comparisons, many other possibilities exist. The case similarity concepts described in the literature that are based on more elaborate structural properties are classified here into five groups: restricted geometric relationships; graphs; semantic nets; model-based similarities; hierarchically structured similarities. Some general topics conclude this survey on structure-based case retrieval methods and systems.

The discipline of case-based reasoning develops techniques to retrieve and reuse old solutions for new problems. To reuse solutions, several case adaptation systems have been built and many are under development. They deal with different tasks in different domains, but a methodology is still lacking. From an analysis of up-to-date case adaptation systems, this article moves towards a methodology by providing a common framework and some guidelines. As key issues case adaptation techniques, global strategies, and the tailoring of cases are discussed.

In October 1995, Takeshi Furuhashi and his collegues at the Bio-Electronics Laboratory of Nagoya University, Japan, organized the first of a series of on-line workshops, held entirely on the World Wide Web. The advertised advantages of the on-line format were to allow fruitful exchanges while avoiding physical travel, and to guarantee wide visibility of the discussion. The first two workshops in the series were devoted to evolutionary computation; they can be accessed on the web at http://www.bioele.nuee.nagoya-u.ac.jp. The third workshop, named “First On-Line Workshop on Soft Computing” (WSC1), had a broader scope, including all the techniques that go under the heading of “soft computing”, like fuzzy logic, neuro computing, genetic computing, and so on. WSC1 took place from August 19 to 30 1996, and it is accessible on the web at http://www.bioele.nuee.nagoya-u.ac.jp/wsc1/. Because the declared goal of an on-line workshop is to prompt discussion, the rules for submission were looser than in most traditional workshops: papers were not subject to peer review, and it was possible to submit already published papers. All the submitted papers were made visible on the web one week before the workshop, and people could send comments and questions by email during the two workshop weeks; all the questions, comments, and authors' replies are also visible at the WSC1 web site.

Ensuring reliability and enhancing quality of Knowledge Based Systems (KBS) are critical factors for their successful deployment in real-world applications. This is a broad task involving both methodological and formal approaches for designing rigorous Validation, Verification and Testing (VVT) methods and tools. Some of these can be adapted from conventional software engineering, while others rely on specific aspects of KBS.