The software and knowledge engineering literature defines maintenance strategies for seven main types of knowledge: words; sentences; behavioural knowledge; and meta-knowledge. Meta-knowledge divides into problem solving methods; quality knowledge; fix knowledge; social knowl- 5 edge; and processing activities. There are five main ways in which these seven knowledge types are processed: acquire; operationalise; fault; fix; and preserve. We review systems that contribute to these 7 5 = 35 types of knowledge maintenance. 1 Introduction 10 A general trend in the twentieth century is an increasing level of doubt about the things we speak or write or try to enter into programs. Popper argues that all knowledge is an hypothesis since nothing can ever be ultimately proved; Submitted to the Knowledge Engineering Review page 2 of 73 our currently believed ideas are merely those that have survive active attempts to refute them [89]. Knowledge representation theorists stress that KBs are...

. 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...

Abstract: Ripple down rules have addressed two of the major limitations of first generation Expert Systems (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 shift in the way these problems were solved, it is expected that RDR can offer a new approach to the issue of knowledge reuse. Due the poor acceptance of ES by end-users, our focus is more on reusing knowledge in different modes, such as explanation, critiquing or ‘what-if ’ within the same domain rather than the more conventional approach of reusing problem-solving methods or ontologies to solve a similar problem in a somewhat differerent domain. An evaluation of RDR for reuse showed that many modes of use were possible without any change to the knowledge or its structure but that some modes required understanding of the models represented. Since RDR does not require analysis or modeling of the domain for KA, maintenance or finding conclusions we have incorporated ideas from Formal Concept Analysis (FCA) to allow concepts and the relationships between them to be identified and explored. The addition of FCA tools to RDR is described in this paper. 1. The Reuse of Knowledge The reuse of knowledge should result in potential savings

The work reported in this thesis is motivated by the belief that knowledge-based systems (KBS) research needs to focus more on users ’ needs and cater for the various decision situations in which users will find themselves. To build individual systems that cater for all the activities that may be needed is not feasible or desirable. The problems associated with capturing knowledge are well known and the ability to capture knowledge once and access and manipulate the knowledge in multiple ways is highly desirable. It adds value to the original knowledge and offers all the benefits associated with the reuse of resources. Thus, the problem becomes one of knowledge reuse. The research question pursued in this thesis is “can knowledge captured for one purpose, such as consultation, be reused to support a wide range of alternative purposes, such as critquing or tutoring, allowing the user to answer different types of questions according to their current circumstances”? Further, this question was to be answered in a situated cognition, dynamic knowledge framework. The system developed in this thesis is based on the Multiple Classification Ripple Down Rule (MCRDR) knowledge acquisition and representation technique. MCRDR is a form of casedbased

: 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...

Currently, "task analysis" is the dominant paradigm in the knowledge acquisition community. We argue that for performance systems (i.e. systems that do not have to offer a knowledge-level description of their performance at runtime) a simpler "test analysis" approach may suffice. We offer examples were a seemingly-naive testing regime gives rise to competent performance systems. Further, by certain measures, these systems developed via test analysis out-performed systems developed for similar domains using other techniques. Test analysis did not augment some other methodological approach: it removed the need for any other methodology. We speculate that for performance systems, task analysis could be deferred till after the development of a tested performance system. That is, for performance systems, testing replaced task but task analysis could augment test analysis once a system was in production. 1. INTRODUCTION Task analysis evolved from a reverse engineering of existing expert sy...

Situated cognition motivates a review of knowledge maintenance. If knowledge is situated, then a symbolic knowledge base will require modification as the situation changes. We examine the AI and software engineering literature which relates to acquiring and changing programs. Based 5 on a reverse engineering of that literature, we see that strategies have been offered to maintain different types of knowledge: words; sentences; behavioural knowledge; and meta-knowledge. Meta-knowledge divides into problem solving methods; quality knowledge; fix knowledge; social knowledge; and processing activities. We see several kinds of processing activities in 10 the literature: acquire; operationalise; fault; fix; and preserve. To date, no single strategy maintains all types of knowledge or performs all processing activities. However, we can say that many of these processing activities perform some kind of search space reflection. We can use this framework as follows. By using the quality knowled...

Knowledge can be difficult to capture and codify, interpret and reuse. Knowledge management is an important determinant of an organisation’s competitive edge. It can impact organisational agility and hence customer satisfaction. The rise of the corporate call / support centre and helpdesk bears witness to this phenomenon. In this paper we consider some of the knowledge management issues facing the corporate support centre. We use the support centre of a large multinational high-tech organisation dealing with the complexities of the burgeoning Information Technology (IT) field as our example. While vendor solutions focus on tracking incoming problem cases, and separately tracking and archiving solutions in a corporate knowledge-base, we observe that there is a huge stone left unturned in the form of problem diagnosis, where-to-search, and what-to-search-for knowledge. As a solution we offer the ripple down rules (RDR) knowledge acquisition technique, which has addressed many of the shortcomings of first generation expert systems. We review some of the key RDR implementation issues and describe our planned extensions to fit the complexity and volatility of the support centre.