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Case Based Reasoning is a methodology for problem solving based on past experiences. This methodology tries to solve a new problem by retrieving and adapting previously known solntions of similar problems. However, retrieved solutions, in general, require adaptations in order to be applied to new contexts. One of the major ehallenges in Case Based Reasoning is the development of an efficient methodology for case adaptation. In contrast to case acquisition, knowledge for case adaptation is not easily available and is hard to obtain (Hanney, 1996; Wiratunga et aí., 2002). The most widely used form of adaptation employs handcoded adaptation rules, which demands a significant effort of knowledge acquisition for case adaptation, presenting a few difflculties (Hanney, 1996). An alternative to overcome the difflculties associated to the acquisition of knowledge for case adaptation has been the use of hybrid approaches and automatic learning algorithms for the acquisition of the knowledge used for the adaptation. This Work investigates the use of hybrid approaches for case adaptation employing Machine Learning algorithms. The approaches automatically le~ arn adaptation knowledge from a case base and apply it to adapt retrieved solutions.

Estratégias de adaptação de casos para sistemas de raciocínio baseado em casos