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.

Object-oriented class libraries offer the potential for individual researchers to manage the large bodies of code generated in the experimental development of complex interactive systems. This article analyzes the structure of such a class library that supports the rapid prototyping of a wide range of systems including collaborative networking, shared documents, hypermedia, machine learning, knowledge acquisition and knowledge representation, and various combinations of these technologies. The overall systems architecture is presented in terms of a heterogeneous collection of systems providing a wide range of application functionalities. Examples are given of group writing, multimedia and knowledge-based systems which are based on combining these functionalities. The detailed design issues of the knowledge representation server component of the system are analyzed in terms of requirements, current state-of-the-art, and the underlying theoretical principles that lead to an effective obj...

. 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...

. 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. Though, experts do not normally have introspective access to that knowledge, their explanations of actual search considerations seems very valuable in constructing a knowledge level model of their search processes. The incremental method was inspired by the work on Ripple-Down Rules which allows knowledge acquisition and maintenance without analysis or a knowledge engineer. We substantially extend Ripple Down Rules to allow undefined terms in the conditions. These undefined terms in turn become defined by Ripple Down Rules. The resulting framework is called Nested Ripple Down Rules. Our system SmS1.2 (SmS for Smart Searcher), has been employed for the acquisition of expert chess knowledge for performing a highly pruned tree search. Our first experimental results in the chess domain are ev...

Abstract: The need for analysis and modeling of knowledge has been espoused by many researchers as a prerequisite to building knowledge based systems (KBS). This approach has done little to alleviate the knowledge acquisition (KA) bottleneck or the maintenance problems associated with large KBS. For actual KA and maintenance we prefer to use a technique, known as ripple down rules (RDR) that is simple, yet reliable, and later see what models can be produced from the knowledge for the purpose of reuse. Tools based on Formal Concept Analysis have been added to RDR to uncover and explore the underlying conceptual structures. 1 Models and their Role in Knowledge Acquisition Since Newell’s [21] paper on “The Knowledge Level ” there has been increasing awareness and acceptance of the need to model knowledge at a level above its symbolic representation. This notion was further explored by Clancey [3] who used task and problem solving methods analysis to divide problems into “heuristic classification ” and “heuristic construction”. Following Van de Velde [33] approaches

Knowledge acquisition remains one of the primary constraints on the development of expert systems. A number of researchers have explored methods for allowing a machine learning system to assist a knowledge engineer in knowledge acquisition. In contrast, we are exploring methods for enabling an expert to directly interact with a machine learning system to collaborate during knowledge acquisition. We report recent extensions to our methodology encompassing a revised model of the role of machine learning in knowledge acquisition; techniques for communication between a machine learning system and a domain expert and novel forms of assistance that a machine learning system may provide to an expert. Keywords : Machine Learning; Knowledge Acquisition; Knowledge Elicitation Introduction Despite two decades of research, knowledge acquisition remains a primary bottleneck for expert system development. The two primary approaches to knowledge acquisition are knowledge elicitation from experts a...

: The poor acceptance of ES technology by users has been attributed to the lack of attention to computer and user cooperation issues in knowledge based systems (KBS). While the traditional question-conclusion style of interaction may be appropriate in many circumstances, it is not necessarily the best or the only mode that users may require. Teaching, causal explanation/modeling, critiquing, `what-if' analysis and even knowledge acquisition are some of the possible modes of interaction. If we are able to adapt knowledge already captured to a wide range of modes we can add value to the knowledge resource. Ripple down rules have addressed two of the major limitations of first generation ES, the maintenance and knowledge acquisition (KA) bottleneck problems. This is achieved through acquiring knowledge directly from an expert, the use of an exception structure for knowledge representation and the storing of the cornerstone case associated with each rule. Just as RDR has offered a paradigm...

. 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...

Abstract — We describe the design and implementation of an intelligent reporting and alerts system that has been designed with a specific goal to address the needs of managing chronic and complex disease through the use of home telecare technology. Our approach has been to develop these tools using as far as possible, open standards. Clinical measurement data gathered using home telecare and stored in a relational database in XML format is extracted and converted into a Clinical Document Architecture (CDA) as defined by the Health Level 7 (HL7) organization. Data trends are presented to the clinician as simple graphs and summary statistics (means, standard deviations) over time for an individual patient. Clinicians may receive this data by display through a Web-interface or by email or faxed