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...

Situated cognition is not a mere philosophical concern: it has pragmatic implications for current practice in knowledge acquisition. Tools must move from being design-focused to being maintenance-focused. Reuse-based approaches (e.g. using problem solving methods) will fail unless the reused descriptions can be extensively modified to suit the new situation. Knowledge engineers must model not only descriptions of expert knowledge, but also the environment in which a knowledge base will perform. Descriptions of knowledge must be constantly re-evaluated. This re-evaluation process has implications for assessing representations 1.

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

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...

Extensive amounts of knowledge and data stored in medical databases require the development of specialized tools for storing and accessing of data, data analysis, and effective use of stored knowledge and data. This paper focuses on methods and tools for intelligent data analysis, aimed at narrowing the increasing gap between data gathering and data comprehension. The paper sketches the history of research that led to the development of current intelligent data analysis techniques, discusses the need for intelligent data analysis in medicine, and proposes a classification of intelligent data analysis methods. The scope of the paper covers temporal data abstraction methods and data mining methods. A selection of methods is presented and illustrated in medical problem domains. Presently data abstraction and data mining are attracting considerable research interest. However the two technologies, in spite of the fact that they share their central objective, namely the intelligen...

Situated cognition (SC) claims that knowledge is mostly context-dependent and that symbolic descriptions elicited prior to direct experience are less important than functional units developed via direct experience with the current problem. If this were true, then we would need to modify the knowledge modeling approaches of KA which assume that re-using old symbolic descriptions are a productivity tool for new applications. There are numerous tools which, if added to conventional knowledge modeling, could be said to handle SC (e.g. machine learning, abduction, verification & validation tools, repertory grids, certain frameworks for decision support systems, expert critiquing systems, and ripple-down-rules). However, we require an experiment to assess the effectiveness of these tools as a response to SC. 1 Introduction "What is wanted is not the will to believe, but the will to find out, which is the exact opposite." -- Bertrand Russell "Measure what is measurable, and make measurable w...

maintenance with the author. The role of a discussion paper is to provoke discussion. Hence, my comments will be heretical rather than theoretical. For the theory, see [19]. Today I want to say two things. Firstly, the knowledge maintenance (KM) problem is our next big challenge. A common method for tackling the KM problem is to use some type of meta-knowledge to represent expected/unexpected or good/bad knowledge. This metaknowledge takes many forms including architectures [26], patterns [12], problem solving methods (PSMs) [29, 31], or ontologies [13]. Elsewhere [20], I have argued that patterns of design and architecture are very similar to PSMs and ontologies: Both represent abstract descriptions of supposedly common parts of many designs.

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.

We advocate changing a language if that language cannot support validation. To this end, we are exploring the computational cost of basic language constructs within our abductive validation framework. Conjunction is found to decrease the computational cost of validation for theories 5 that are not used for time-based simulation purposes. However, in the case of time-based simulations, the use of "and" is problematic. 4000 words 1 Introduction Modern KA views KBS construction as the construction of inaccurate surrogates 10 models of reality (x2). We argue that such potentially inaccurate models must be tested, lest they generate inappropriate output for certain circumstances. Testing can only demonstrate the presence of bugs (never their absence) and so must be repeated whenever new data is available. That is, testing is an essential, on-going process through-out the lifetime of a knowledge base. 15 Elsewhere [14] we have offered an assessment criteria for a language based on its "...