Case-Based Reasoning (CBR) is an artificial intelligence technique used to solve complex problems by applying previous solutions to similar problems.

The idea behind CBR is that a system can use previously acquired experience to solve new problems. Instead of designing an algorithm or model from scratch for each new problem, CBR looks for similar solutions to previously solved problems and adapts them to the current situation.

The CBR process is divided into four stages: retrieval, reuse, review and retention. In the retrieval stage, the system finds previous cases similar to the current problem. In the reuse stage, the system adapts and applies the previous solutions to the current situation. In the review stage, the system evaluates the effectiveness of the proposed solution and makes adjustments if necessary. Finally, in the retention stage, the new solved case is stored in the system's knowledge base for possible future use.

CBR is often used in applications where it is difficult to design algorithmic solutions due to the complexity of the input data or the difficulty of modeling the problem at hand. For example, CBR has been used in medical diagnostic systems, quality control system design, transportation route planning, and problem solving in the engineering industry.

The success of CBR depends largely on the quality of the knowledge base used by the system. If the knowledge base is poorly designed or lacks sufficient cases to cover a wide range of situations, the system may not be effective in solving complex problems.