A brief review of learning by examples methodologies was given. The main disadvantages of non-incremental algorithms were presented. Incremental learning and the problem of forgetting in learning was addressed.
We presented a methodology of knowledge integration that provides a means for the partial solution of the problem of forgetting in learning by examples. This method in spite of its forgetting capability also brings a improvement in performance as it was shown by extensive experimental results.
Knowledge integration also deals with problems such as multi-agent learning enabling a community of agents to interact, in order to improve the agent's view of the world. A possible architecture for such a multiple agent environment was presented and associated communication problems discussed.
Also a possible architecture for a learning by examples algorithm which took advantage of KI strategy was given. This possibility should be further developed as it might bring more insight to the relations and advantages of the use of KI methodology in learning.