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.
A shorter version of this paper has been accepted to the conferrence EKAW 2006.
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Compton, P., Cao, T.M. (2006). Evaluation of Incremental Knowledge Acquisition with Simulated Experts. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_8
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DOI: https://doi.org/10.1007/11941439_8
Publisher Name: Springer, Berlin, Heidelberg
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