FROM CASE-BASED REASONING TO TRACES-BASED REASONING

CBR is an original paradigm to adapt solutions of past problems in order to solve new similar problems. A case is a problem with its solution and cases are stored in a case library. The reasoning process obeys to a cycle allowing to “learn” from new solved cases. This approach is also viewed as a lazy learning method when applied for classification. This AI technology is applied for various tasks as designing, planning, diagnosing, information retrieving, etc. The talk will be the occasion to go a step further in reusing past experience, by considering traces of computer use as experience knowledge containers for contextual and situation based problem solving.