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

We develop a general abductive description of testing models. We find that this testing process is fundamentally slow and cannot be conducted exhaustively. Consequently, we argue that the usual case for model testing is nonexhaustive testing; i.e. some subset of the possible tests are chosen and executed. Note that if the tests result in model refinement, then different tests can result in different models. This leads to the hypothesis that different individuals form different "opinions" (i.e. models) about the world as a result of the different examples they push through their models. We prefer this symbolic explanation for situated semantics to non-symbolic proposals (e.g. neural).

A single inference procedure (abduction) can be used for (amongst other things) prediction, classification, explanation. planning, monitoring, diagnosis, qualitative reasoning, validation, verification, diagrammatic reasoning, multiple-expert knowledge acquisition and decision support systems.

It is an interesting and exciting challenge to change programming modalities from a traditional textbased approach to a 2-D screen. Based on a survey of current visual programming systems, we find that numerous software engineering and knowledge engineering techniques are required to meet that challenge. Further, we argue that VP systems can benefit from on-going knowledge engineering research on the computational complexity of different representations. Hence, we conclude that the designers of VP systems should be well-versed in a wide range of knowledge engineering and software engineering techniques. 1 Introduction It is an interesting and exciting challenge to change programming modalities from a traditional textbased approach to a 2-D screen. When faced with such a challenge, we find that a wide range of software engineering and knowledge engineering techniques are required. For example: ffl The main problem with constraint-based visual programming languages (e.g. the THINGLAB...

The dominant knowledge modeling paradigm in the KA field assumes that old knowledge expressed symbolically (i.e. problem-solving strategies or ontologies) is a productivity tool for building new knowledge bases. That is, it assumes that knowledge is context-independent. Researchers of situated cognition (SC) claims that knowledge is mostly context-dependent and that concepts elicited prior to direct experience are less important than functional units developed via direct experience with the current problem. We argue that there is sufficient evidence to support some of the SC view; i.e. we may need to modify the knowledge modeling approach. Symbolic approaches exist which could be said to handle SC; e.g. abduction, verification & validation tools, repitory grids, certain frameworks for decision support systems, expert critiquing systems, ripple-down-rules, and rippledown -models. However, such approaches need careful assessment in order to test their appropriateness as a response to the...

We discuss using a single inference procedure (abduction) for implementing the various modules of an intelligent decision support systems. 1 Introduction We define intelligent decision support systems (IDSSs) as model-based software systems that support management comfort in vague domains. In terms of a computational architecture, the main requirements for such a system are the ability to: ffl Validate models; ffl Perform inference over those models using assumptions; ffl Managing mutually exclusive assumptions in separate worlds; ffl Support domain specific criteria for finding the best worlds. We find that a single inference procedure (abduction) satisfies all these requirements. Hence, we propose the use of abduction as a framework for IDSS. All the above terms in italics are defined below. Section 1 describes in detail our definition of IDSS. Section 2 describes abduction. Section 3 discusses using abduction for IDSS. Section 4 discuss the practicality of our proposal. Note th...

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...

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