A Cooperation between Case - Based Reasoningand Incremental Concept Learning

In case-based reasoning systems, reasoning and learning are closely linked. These systems use various machine learning techniques to achieve their learning tasks. In particular , they often show a hierarchical memory organization close to conceptual clustering learnt concept hierarchies. The cooperation between these two artiicial intelligence methodologies ooers several advantages. For case-based reasoning, incremental concept learning optimizes the memory structure, the role of which is essential in all reasoning steps. It also enlarges the application range of case-based reasoning. For in-cremental concept learning, case-based reasoning proposes its whole architecture, and above all its memory, from which the learning process can beneet, particularly for remedying the problem of the dependency upon the order of presentation of the instances , which is a crucial problem for this type of learning. It also permits the use of theoretical knowledge to explain the concepts learnt. This article presents a case-based reasoning system taking advantage from this cooperative approach.