Titlebook: Case-Based Reasoning Research and Development; 8th International Co Lorraine McGinty,David C. Wilson Conference proceedings 2009 Springer-V

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https://doi.org/10.1007/978-1-4842-2562-2perience to improve performance and/or learning on a different (target) task. TL methods are typically complex, and case-based reasoning can support them in multiple ways. We introduce a method for recognizing intent in a source task, and then applying that knowledge to improve the performance of a

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https://doi.org/10.1007/978-1-4842-2820-3o acquire. In this paper, two strategies are combined in order to reduce the knowledge engineering cost induced by the adaptation knowledge (AK) acquisition task: AK is learned from the case base by the means of knowledge discovery techniques, and the AK acquisition sessions are opportunistically tr

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https://doi.org/10.1007/978-1-4842-2820-3ase Based Reasoning (CBR) and Reinforcement Learning (RL) techniques. This approach, called Case Based Heuristically Accelerated Reinforcement Learning (CB-HARL), builds upon an emerging technique, the Heuristic Accelerated Reinforcement Learning (HARL), in which RL methods are accelerated by making

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