From case-based reasoning to traces-based reasoning

CBR is an original AI 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.