Introspective multistrategy learning: on the construction of learning strategies
Artificial Intelligence
Towards context-based search engine selection
Proceedings of the 6th international conference on Intelligent user interfaces
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning and Applying Case-Based Adaptation Knowledge
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
The Adaption Knowledge Bottleneck: How to Ease it by Learning from Cases
ICCBR '97 Proceedings of the Second International Conference on Case-Based Reasoning Research and Development
ICCBR '01 Proceedings of the 4th International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
Retrieval, reuse, revision and retention in case-based reasoning
The Knowledge Engineering Review
Opportunistic Acquisition of Adaptation Knowledge and Cases -- The IakA Approach
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Knowledge Planning and Learned Personalization for Web-Based Case Adaptation
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Case base mining for adaptation knowledge acquisition
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Learning semantic descriptions of web information sources
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Enhancing case adaptation with introspective reasoning and web mining
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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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 [20] 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.