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Taking up the situated cognition challenge with ripple down rules
Authors:DEBBIE RICHARDS  PAUL COMPTON
Affiliation:Department of Artificial Intelligence, School of Computer Science and Engineering, University of New South Wales, Sydney, NSW 2052, Australiaf1
Abstract:Situated cognition poses a challenge that requires a paradigm shift in the way we build symbolic knowledge-based systems. Current approaches require complex analysis and modelling and the intervention of a knowledge engineer. They rely on building knowledge-level models which often result in static models that suffer from the frame of reference problem. This approach has also resulted in an emphasis on knowledge elicitation rather than user requirements elicitation. The situated nature of knowledge necessitates a review of how we build, maintain and validate knowledge-based systems. We need systems that are flexible, intuitive and that interact directly with the end-user. We need systems that are designed with maintenance in mind, allowing incremental change and on-line validation. This will require a technique that captures knowledge in context and assists the user to distinguish between contexts. We take up this challenge with a knowledge acquisition and representation method known as Ripple-down Rules. Context in Ripple-down Rules is handled by its exception structure and the storing of the case that prompted a rule to be added. A rule is added as a refinement to an incorrect rule by assigning the correct conclusion and picking the salient features in the case that differentiate the current case from the case associated with the wrong conclusion. Thus, knowledge acquisition and maintenance are simple tasks, designed to be performed incrementally while the system is in use. Knowledge acquisition, maintenance and inferencing are offered in modes that can be performed reflexively without a knowledge engineer. We further describe the addition of modelling tools to assist the user to reflect on their knowledge for such purposes as critiquing, explanation, “what-if” analysis and tutoring. Our aim is to provide a system that lets the user choose the mode of interaction and view of the knowledge according to the situation in which they find themselves and their own personal preferences.
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