Making case adaptation practical is a longstanding challenge for casebased reasoning. One of the impediments to widespread use of automated case adaptation is the adaptation knowledge bottleneck: the adaptation process may require extensive domain knowledge, which may be difficult or expensive for system developers to provide. This paper advances a new approach to addressing this problem, proposing that systems mine their adaptation knowledge as needed from pre-existing large-scale knowledge sources available on the World Wide Web. The paper begins by discussing the case adaptation problem, opportunities for adaptation knowledge mining, and issues for applying the approach. It then presents an initial illustration of the method in a case study of the testbed system WebAdapt. WebAdapt applies the approach in the travel planning domain, using OpenCyc, Wikipedia, and the Geonames GIS database as knowledge sources for generating substitutions. Experimental results suggest the promise of the approach, especially when information from multiple sources is combined.

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Abstract. Non-learning problem solvers have been applied to many interesting and complex domains. Experience-based learning techniques have been developed to augment the capabilities of certain non-learning problem solvers in order to improve overall performance. An alternative approach to enhancing preexisting systems is automatic case elicitation, a learning technique in which a case-based reasoning system with no prior domain knowledge acquires knowledge automatically through real-time exploration and interaction with its environment. In empirical testing in the domain of checkers, results suggest not only that experience can substitute for the inclusion of pre-coded model-based knowledge, but also that the ability to explore is crucial to the performance of automatic case elicitation.

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