Case-Based Reasoning (CBR) is an approach to problem-solving in artificial intelligence that involves reusing past experiences (cases) to solve new problems​.Instead of relying on general rules or an explicit model, a CBR system stores a knowledge base of cases, where each case is a specific problem situation paired with its solution (and often an explanation or outcome). When a new problem arises, the system retrieves a prior case that is similar to the current one and then reuses (adapts) that case’s solution to fit the new problem’s context​.This process is inspired by how humans often reason by analogy, recalling how a similar issue was resolved in the past and applying that experience to the current situation.In practice, case-based reasoning typically follows a structured four-step cycle​Retrieve: Identify the most similar past case(s) from the case library that resemble the new problem. (E.g., a help-desk system finds a past incident report that matches a new customer’s issue.)​Reuse: Copy or adapt the solution from the retrieved case to propose a solution for the current problem. Some adaptation may be needed if there are differences between the old and new cases​Revise: Test the proposed solution in the real world (or through simulation) and revise it if necessary. If the solution doesn’t fully solve the problem, adjust it until it works (this step is essentially error-correcting based on feedback)​Retain: After successfully solving the new problem, incorporate this experience as a new case into the case base for future reference​. The system thus “learns” by storing the solved case, enriching its knowledge for the next queries.CBR has been used in domains such as legal reasoning (where past legal cases inform decisions on new cases), customer support systems, and medical diagnosis. One advantage of CBR is its ability to provide explanations for solutions: since a solution is derived from a specific past case, the system can present that analogy (“We solved a similar issue this way before”). This approach naturally handles incremental learning (each new case solved becomes a training example for future problems) and can work even when an explicit general theory of the domain is hard to formulate. However, maintaining an efficient and relevant case library (avoiding case overload or redundancy) and designing good similarity metrics are important challenges in case-based reasoning systems.