Abstract
Acquiring knowledge directly from the domain expert requires a knowledge representation and specification method that is comprehensible and feasible for the holder and creator of that knowledge. The technique, known as multiple classification ripple down rules (MCRDR), is novelly applied to the problem of building and maintaining a library of training scenarios for use by customs and immigration officer trainees in our agent-based virtual environment which may be indexed for retrieval based on the rules associated with them. Our evaluation study aims to demonstrate the utility of the MCRDR combined case and exception structure rule-based approach over standard rules alone and a non-case-based approach.
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References
Colomb, R.M.: Decision Tables, Decision Trees and Cases: Propositional KBS Tech. Rep. 266. Comp. Sci. Dept. UQ, Australia (1993)
Gaines, B.R.: Transforming rules and trees into Comprehensible Knowledge Structures. In: Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.), pp. 205–226. MIT Press, Menlo Park (1996)
Kang, B., Compton, P., Preston, P.: Multiple Classification Ripple Down Rules: Evaluation and Possibilities. In: Proc. KAW 1995, February 26-March 3, vol. 1, pp. 17.1–17.20 (1995)
Kwok, R.B.: Translations of Ripple Down Rules into Logic Formalisms. In: Dieng, R., Corby, O. (eds.) EKAW 2000. LNCS (LNAI), vol. 1937, pp. 366–379. Springer, Heidelberg (2000)
Quinlan, J.R.: Discovering rules by induction from large collections of examples. In: Mitchie, D.E. (ed.) Expert systems in the micro-electronic age. Edinburgh Uni. Press, Edinburgh (1979)
Quinlan, J.R.: Fwd. Knowledge Discovery in Databases, pp. ix-xii. MIT Press, Cambridge (1991)
Sugiura, A., Riesenhuber, M., Koseki, Y.: Comprehensibility Improvement of Tabular Knowledge Bases. In: AAAI 1993, pp. 716–721 (1993)
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Richards, D., Taylor, M. (2010). Incremental Learning via Exceptions for Agents and Humans: Evaluating KR Comprehensibility and Usability. In: Zhang, BT., Orgun, M.A. (eds) PRICAI 2010: Trends in Artificial Intelligence. PRICAI 2010. Lecture Notes in Computer Science(), vol 6230. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15246-7_65
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DOI: https://doi.org/10.1007/978-3-642-15246-7_65
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