Evaluation of incremental knowledge acquisition with simulated experts

Authors:
Paul Compton;Tri M. Cao
Affiliations:
School of Computer Science and Engineering, University of New South Wales, Sydney, Australia;School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
Venue:
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Year:
2006

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Abstract

Evaluation of knowledge acquisition (KA) is difficult in general. In recent times, incremental knowledge acquisition that emphasises direct communication between human experts and systems has been increasingly widely used. However, evaluating incremental KA techniques, like KA in general, has been difficult because of the costs of using human expertise in experimental studies. In this paper, we use a general simulation framework to evaluate Ripple Down Rules (RDR), a successful incremental KA method. We focus on two fundamental aspects of incremental KA: the importance of acquiring domain ontological structures and the usage of cornerstone cases.