Ripple Down Rules (RDR) is a knowledge acquisition method which constrains the interactions between the expert and a shell to acquire only correct knowledge. Although RDR works well, it is only suitable for the problem of providing a single classification for a set of data. Multiple Classification Ripple Down Rules (MCRDR) is an extension of RDR which allows multiple independent classifications. The approach has been evaluated in simulation studies where the human expert is replaced by a simulated expert. MCRDR may provide a basis for building a general problem solver for a range of problems beyond classification.

Evaluation of knowledge acquisition methods remains an important goal; however, evaluation of actual knowledge acquisition is difficult because of the unavailability of experts for adequately controlled studies. This paper proposes the use of simulated experts, i.e., other knowledge based systems as sources of expertise in assessing knowledge acquisition tools. A simulated expert is not as creative or wise as a human expert, but it readily allows for controlled experiments. This method has been used to assess a knowledge acquisition methodology, Ripple Down Rules at various levels of expertise and shows that redundancy is not a major problem with RDR. Introduction Evaluation of knowledge acquisition (KA) methods remains an important goal. Many KA methods have been proposed and many tools have been developed. However, the critical issue for any developer of knowledge based systems (KBS) is to select the best KA technique for the task in hand. This means that papers describing m...

. This paper suggests that a distinction between knowledge acquisition methods should be made. On the one hand there are methods which aim to help the expert and knowledge engineer analyse what knowledge is involved in solving a particular type of problem and how this problem solving is carried out. These methods are concerned with classifying the different types of problem solving and providing tools and methods to help the knowledge engineer identify the appropriate approach and ensure nothing is omitted.. A different approach to knowledge acquisition focuses on ensuring incremental addition of validated knowledge as mistakes are discovered (validated knowledge here means only that the earlier performance of the system is not degraded by the addition of new knowledge). The organisation of this knowledge is managed by the system rather than the expert and knowledge engineer. This would seem to correspond to human incremental development of expertise. From this perspective...

We present a learning algorithm for rule-based concept representations called rippledown rule sets. Ripple-down rule sets allow us to deal with the exceptions for each rule separately by introducing exception rules, exception rules for each exception rule etc. up to a constant depth. These local exception rules are in contrast to decision lists, in which the exception rules must be placed into a global ordering of the rules. The localization of exceptions makes it possible to represent concepts that have no decision list representation. On the other hand, decision lists with a constant number of alternations between rules for different classes can be represented by constant depth ripple-down rule sets with only a polynomial increase in size. Our algorithm is an Occam algorithm for constant depth ripple-down rule sets and, hence, a PAC learning algorithm. It is based on repeatedly applying the greedy approximation method for the weighted set cover problem to find good exception rule set...

A knowledge acquisition methodology, Ripple Down Rules, has been developed which only allows knowledge to be used in the context in which it is acquired and ensures that only valid rules can be added to a knowledge base. This method has now been used to build a large (2000 rule) medical expert system. This system is in routine use in a Chemical Pathology laboratory providing clinical interpretations for laboratory reports. It has been developed entirely by experts with no knowledge acquisition or programming support or skills. This task was a minor extension to their normal duties. This paper describes the resultant knowledge base and concludes that such knowledge acquisition is very simple. It claims that allowing knowledge to be easily refined is a powerful technique which greatly simplifies dealing with complex domains. INTRODUCTION TO RIPPLE DOWN RULES Ripple down rules (RDR) is a knowledge acquisition methodology and a way of structuring knowledge bases which grew o...

. Ripple down rules (RDR) is a knowledge acquisition technique that addresses the bottleneck problem by allowing rapid development of knowledge bases (KB) by experts, without the need for lengthy analysis or intervention of a knowledge engineer. This is achieved through the use of an exception structure and the storing of cornerstone cases. The exception structure avoids the problem of side-effects that occur when traditional rule-based ES are maintained, as knowledge in an RDR KB is never deleted or changed, only added. This can lead to repetitious knowledge. While studies have shown that the repetition problem is small, the concern of this study is the impact of repetition on reuse of the knowledge base for purposes such as explanation, modeling or tutoring. This paper reports on work that has been done using two different machine learning techniques, Induct and Rough Sets, to compact various ripple down rule knowledge bases by removing repetitious or redundant knowledge. 1. Introduc...

Abstract: Ripple Down Rules (RDR) have a longstanding (and successful) history in the field of biomedical engineering. RDR are a knowledge acquisition and representation technique that allow knowledge to be rapidly acquired and maintained by the domain expert. A key feature of RDR, and the reason why maintenance is easily managed, is that rules are never modified or deleted but they are locally patched. That is, new rules are exceptions to previous rules and the new rule is validated within the context of previously seen cases. One drawback of locally patching is that knowledge can be repeated in different locations of the knowledge base. This paper describes some work done on removing repeated knowledge. The experiments reported were performed on a pathology knowledge base but the algorithm is applicable to any multiple classification RDR knowledge based system. The results support the findings of others that exception structures are compact representations with few opportunities to reduce further. This also suggests that experts tend to provide overly general rules in the first instance which they modify by adding specialistions in the form of exception rules as new cases are seen. 1

This article addresses the problem of the induction of comprehensible hypotheses on complex domains. It proposes a hypothesis language (a type of decision graph) and an inductive algorithm (PASTEUR) which naturally induces structured rules. The advantages over other hypothesis languages in terms of comprehensibility are reviewed as well as the possibility given to represent explicitly exceptions, and some basic features of the algorithm are presented. The experimental evaluation is done on two classical ML databases from UCI Irvine. A --- Introduction The Machine Learning community has been traditionally concerned with the performances of learning algorithms in terms of prediction accuracy. But the importance of the format and the nature of the results produced has been also more and more recognized as essential to guarantee a good readability, and consequently, to permit an efficient use of the output of the algorithm. This is particularly true in exploratory induction, in which the ...

Ripple Down Rules (RDR) is a knowledge acquisition method for knowledge based systems (KBS) which facilitates incremental acquisition of knowledge and ensures that the previous performance of the KBS is not degraded by the incremental addition of the new knowledge. This approach is now well established for single classification tasks and more recently has been extended to multiple classification tasks. This paper describes the further extension of the approach to configuration tasks. The test domain for this study is the configuration of ion chromatography methods in analytical chemistry. 1. RDR Background Ripple Down Rules (RDR) is based on the idea that when a KBS makes an incorrect conclusion the new rule that is added to correct that conclusion should only be used in the same context in which the mistake was made(Compton and Jansen 1990). In practice this means attaching the rule at the end of the sequence of rules that were evaluated leading to the wrong conclusion. Thus, this r...

: As in software engineering, there are many potential benefits of reuse in knowledge engineering. Most research into the reuse of knowledge is concerned with the reuse of problem solving methods, the reuse of ontologies or the development and sharing of commonsense knowledge. However, the amount of effort being put into getting the knowledge level model right does not adequately acknowledge the deficiencies of models. The preoccupation with complex models as a prerequisite to knowledge acquisition produces a bottleneck of its own with knowledge acquisition itself becoming a side-issue or afterthought. A situated view of knowledge does not support such an emphasis on developing good models but demands that systems are grounded in the real-world and support incremental change. A technique known as Ripple-Down Rules is based on such beliefs and is offered as an alternative to mainstream approaches to knowledge acquisition with alternative answers to knowledge reuse. 1 The Reus...