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From case-based reasoning to traces-based reasoning
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DOI：10.1016/j.arcontrol.2006.09.003.png)
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摘要
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CBR is an original At paradigm based on the adaptation of solutions of past problems in order to solve new similar problems. Hence, a case is a problem with its solution and cases are stored in a case library. The reasoning process follows a cycle that facilitates learning from new solved cases. This approach can be also viewed as a lazy learning method when applied for task classification. CBR is applied for various tasks as design, planning, diagnosis, information retrieval, etc. The paper is the occasion to go a step further in reusing past Unstructured experience, by considering traces of computer use as experience knowledge containers for situation based problem solving. (C) 2006 Elsevier Ltd. All rights reserved.
Keyword:
problem solvers
artificial intelligence
knowledge-based systems
knowledge representation
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