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
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
Retrieval, reuse, revision and retention in case-based reasoning
The Knowledge Engineering Review
Mining Large-Scale Knowledge Sources for Case Adaptation Knowledge
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
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
A general introspective reasoning approach to web search for case adaptation
ICCBR'10 Proceedings of the 18th international conference on Case-Based Reasoning Research and Development
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Case-based problem-solving systems reason by retrieving relevant prior cases and adapting their solutions to fit new circumstances. The ability of case-based reasoning (CBR) to reason from ungeneralized episodes can benefit knowledge acquisition, but acquiring the needed case adaptation knowledge has proven challenging. This paper presents a method for alleviating this problem with justin-time gathering of case adaptation knowledge, based on introspective reasoning and mining of Web knowledge sources. The approach combines knowledge planning with introspective reasoning to guide recovery from case adaptation failures and reinforcement learning to guide selection of knowledge sources. The failure recovery and knowledge source selection methods have been tested in three highly different domains with encouraging results. The paper closes with a discussion of limitations and future steps.