Acquiring adaptation knowledge for case-based reasoning systems is a challenging problem. Such knowledge is typically elicited from domain experts or extracted from the case-base itself. However, the ability to acquire expert knowledge is limited by expert availability or cost, and the ability to acquire knowledge from the case base is limited by the the set of cases already encountered. The WebAdapt system  applies an alternative approach to acquiring case knowledge, using a knowledge planning process to mine it as needed from Web sources. This paper presents two extensions to WebAdapt’s approach, aimed at increasing the method’s generality and ease of application to new domains. The first extension applies introspective reasoning to guide recovery from adaptation failures. The second extension applies reinforcement learning to the problem of selecting knowledge sources to mine, in order to manage the exploration/exploitation tradeoff for system knowledge. The benefits and generality of these extensions are assessed in evaluations applying them in three highly different domains, with encouraging results.
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- A General Introspective Reasoning Approach to Web Search for Case Adaptation
- Springer Berlin Heidelberg
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