Case-Based Reasoning (CBR) is an AI problem-solving paradigm that solves new problems by retrieving, adapting, and reusing solutions from a library of previously solved cases, operating on the principle that similar problems have similar solutions. CBR systems maintain a structured case base where each case contains a problem description, solution, and outcome, and new problems are solved by finding the most similar past case and adapting its solution to fit the current situation.

Why Case-Based Reasoning Matters in AI/ML: CBR provides interpretable, experience-based decision-making that mirrors human expert reasoning, offering transparent justifications for recommendations by pointing to specific precedent cases rather than opaque model weights.

• Retrieve-Reuse-Revise-Retain (4R cycle) — The CBR process follows a systematic cycle: Retrieve the most similar past case(s), Reuse the retrieved solution (possibly adapted), Revise the solution if it doesn't work perfectly, and Retain the new solved case for future use • Similarity-based retrieval — Cases are retrieved using similarity metrics (weighted feature matching, structural similarity, semantic similarity) that identify the most relevant precedents; k-nearest neighbor is the most common retrieval mechanism • Adaptation mechanisms — Retrieved solutions are adapted to the new problem through substitution (replacing values), transformation (structural changes), or generative adaptation (combining elements from multiple cases) • Lazy learning — CBR defers generalization until query time (unlike eager learners that build models during training), making it naturally incremental—new cases can be added without retraining • Expert system applications — CBR excels in domains where expert knowledge is case-based rather than rule-based: medical diagnosis (similar patient → similar diagnosis), legal reasoning (precedent cases), and troubleshooting (similar fault → similar fix)

Case-based reasoning provides a transparent, precedent-based approach to AI problem-solving that naturally accumulates expertise over time, offering interpretable decisions grounded in specific past experiences rather than abstract learned parameters, making it particularly valuable in domains where explainability and professional accountability are essential.