Case-Based Reasoning (CBR) and Neural Networks for Complex Problems

Case-Based Reasoning (CBR) is a significant branch of Artificial Intelligence (AI). Reasoning in CBR is based on experience or remembering. CBR is the process of solving new problems based on the solutions of similar past problems. It is captured by the "CBR cycle" consisting of the fourth R's: Retrieve, Reuse, Revise, and Retain. When a new problem is encountered, similar past cases are retrieved from the case base, their information is reused to construct solutions, their solutions are revised to fit current needs, and the new experience is retained for future use. The work focuses on the combination of Case-Based Reasoning (CBR) and Artificial Neural Networks (ANN) as complementary methods, in the knowledge engineering domain, to solve any forecasting problem. The Case-Based Reasoning system is used to select a number of stored cases relevant to the current forecasting situation. The Neural Network recycles itself in real time, using a number of closely matching cases selected by the CBR retrieval mechanism, in order to produce the required forecasted values.