The major focus of recent knowledge acquisition research has been on problem-solving methods (PSM). This paper present results where a PSM developed for classification has been extended to handle a configuration or parametric design task, designing ion chromatography methods in analytical chemistry. Surprisingly good results have been obtained seemingly because any knowledge that has been added to the knowledge base, has been added precisely to overcome any limitations of the PSM. These results suggest a trade-off between domain knowledge and the power of the PSM and that greater use of domain knowledge would facilitate re-use by allowing PSMs to be used for a broader range of tasks. INTRODUCTION The critical insight with the emergence of knowledge based systems (KBS), was the usefulness of knowledge as compared to pure search. This was formulated in ideas such as the "knowledge principle". This resulted in expert system shells which while specifically intended for the accumulation o...

Ripple-Down Rules (RDR) has the goal of simple, incremental development of a knowledge-based system (KBS) while the KBS is already in use, so that over time an expert can evolve a sophisticated KBS as a minor extension of their normal duties. RDR has had considerable success in developing classification KBS. It has been extended to configuration, heuristic search and other tasks. This paper proposes a generalisation of RDR that may enable experts to evolve KBS for a range of tasks.

Ripple-Down Rules (RDR) has had considerable success in providing simple incremental knowledge acquisition in classification domains. It has been extended to multiple classification, configuration, search and more recently to resource allocation tasks. Based on the experience of applying RDR to a resource allocation task, this paper proposes a generalisation of RDR to enable it to apply to a wide range of knowledge-based system tasks.

. The development of highly effective heuristics for search problems is a difficult and time-consuming task. We present a knowledge acquisition approach to incrementally model expert search processes. Our approach targets at the implicit representation of the less clearly definable quality criteria by allowing the expert to limit their input to the system to explanations of the steps in the expert search process. These explanations are expressed in our Search Knowledge Interactive Language (SKIL). The explanations are used to construct a knowledge base representing search control knowledge. For the basis of our knowledge acquisition approach, we substantially extend the work done on Ripple-Down Rules which allows knowledge acquisition and maintenance without analysis or a knowledge engineer. This extension allows the expert to enter his domain terms during the KA process, which integrates the knowledge elicitation and knowledge acquisition in one incremental process and allows the expe...

: Incremental refinement methods of knowledge bases ease maintenance but fail to uncover the underlying domain model used by the expert. In this paper, we propose a new knowledge representation formalism for incremental acquisition and refinement of knowledge. It simultaneously guides the expert in expressing his model of the domain during the actual knowledge acquisition process. This knowledge representation scheme, Nested Ripple Down Rules, is a substantial extension to Ripple Down Rule (RDR) knowledge acquisition framework. This paper introduces a theoretical framework for analysing the structure of RDR in general and NRDR in particular. Using this framework we analyse the conditions under which RDR converges towards the target knowledge base. Further, we analyse the conditions under which NRDR offers an effective approach for domain modelling. We discuss the maintenance problems of NRDR as a function of this convergence. We show that the maintenance of NRDR requires similar effort...

Knowledge acquisition (KA) plays an important role in building knowledge based systems (KBS). However, evaluating di#erent KA techniques has been difficult because of the costs of using human expertise in experimental studies. In this paper, we first address the problem of evaluating knowledge acquisition methods. Then, we develop an analysis of the types of errors a human expert makes in building a KBS. Our analysis suggests that a simulation of the key factors in building a KBS is possible. We demonstrate the approach by evaluating three variants of a practically successful KA methodology, namely Ripple Down Rules (RDR). The experimental results provide some fundamental insights into this family of KA techniques and suggest various hints for improvement.

Abstract. We outline an approach to building knowledge-based system based on tightly controlling the order of evaluation of the knowledge components of the system. The order of evaluation is based on two relations, sequence and correction that correspond to the changes that an expert may wish to make to a knowledge base and knowledge acquisition is structured so that new knowledge is added having one of these relations with existing knowledge in the system. We further propose that the knowledge components added might be any knowledge-based systems or programs rather than rules. This proposal is a generalisation of the Ripple-Down Rule incremental approach to building knowledge-based systems. 1

Abstract. The aim of this study was to develop machine learning techniques that would speed up knowledge acquisition from an expert. As the expert provided knowledge the system would generalize from this knowledge in order to reduce the need for later knowledge acquisition. This generalization should be completely hidden from the expert. We have developed such a learning technique based on Duce’s intra-construction and absorption operators [1] and applied to Ripple Down Rule (RDR) incremental knowledge acquisition [2]. Preliminary evaluation shows that knowledge acquisition can be reduced by up to 50%. 1

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Considerable effort is associated with the development, validation and integration of ontologies. This paper suggests that an alternative, or possibly complementary approach, to engineering ontologies is to retrospectively and automatically discover them from existing data and knowledge sources in the organization and then to combine them if desired. The method offered assists in the identification of similar and different terms and includes strategies for developing a shared ontology. The approach uses a data analysis technique known as formal concept analysis to generate an ontology. The approach is particularly strong when used in conjunction with a rapid and incremental knowledge acquisition and representation technique, known as ripple-down rules. However, any data that can be converted into a crosstable (a binary decision table) can also use the approach.