Case-Based Reasoning (CBR)
Also known as: CBR, case-based reasoning cycle, analogy-based reasoning, vaka tabanlı akıl yürütme
Case-based reasoning solves a new problem by retrieving similar problems solved in the past and adapting their solutions, rather than reasoning from first principles or a trained statistical model. Formalized as the Retrieve-Reuse-Revise-Retain cycle by Aamodt and Plaza in 1994 and popularized by Janet Kolodner, CBR mirrors how human experts in medicine, law, and engineering reason by analogy from remembered cases, and it learns simply by storing each newly solved case.
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When to use it
Use case-based reasoning when good solutions to past instances exist and are reusable, the domain is hard to capture in explicit rules or a statistical model, and explanations grounded in concrete precedents are valued — classic fits include medical diagnosis, legal reasoning (precedent), help-desk and technical support, design, and configuration. It is attractive when data accrue incrementally (each solved case is immediately useful) and when transparency matters, since a recommendation can be justified by the analogous case it came from. It depends critically on a meaningful similarity measure and on adaptation knowledge; with poor similarity metrics, no adaptation, or a sparse/biased case base it degrades, and for large smooth datasets a statistical learner may generalize better.
Strengths & limitations
- Learns incrementally by simply retaining solved cases — no batch retraining.
- Transparent and explainable: recommendations are justified by concrete analogous cases.
- Works in domains that resist explicit rules or parametric models.
- Naturally handles a growing, evolving body of experience.
- Quality hinges on the similarity measure and on adaptation knowledge, which can be hard to engineer.
- A biased or sparse case base yields poor or skewed recommendations.
- Retrieval can become slow as the case base grows without good indexing.
- May generalize worse than statistical models on large, smooth datasets.
Frequently asked
How is CBR different from k-nearest neighbours?
Nearest-neighbour classification stops at retrieval — it predicts from similar instances. CBR goes further: it adapts the retrieved solution to the new problem (reuse), evaluates and repairs it (revise), and stores the result (retain). So kNN is essentially CBR's retrieval step without adaptation, revision, or explicit learning by case retention.
Does CBR need a training phase?
No. CBR has no separate training step; it reasons directly from stored cases and learns by retaining each newly solved case. This makes it well suited to domains where knowledge accrues incrementally and where retraining a model would be impractical.
What makes a good similarity measure for CBR?
One that reflects which problem features actually determine the right solution, usually a weighted combination of feature distances with weights set by domain knowledge or learned from data. A poor or unweighted similarity measure is the most common cause of weak CBR performance.
Sources
- Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications, 7(1), 39–59. DOI: 10.3233/AIC-1994-7104 ↗
- Kolodner, J. L. (1992). An introduction to case-based reasoning. Artificial Intelligence Review, 6(1), 3–34. DOI: 10.1007/BF00155578 ↗
How to cite this page
ScholarGate. (2026, June 2). Case-Based Reasoning (CBR). ScholarGate. https://scholargate.app/en/soft-computing/case-based-reasoning
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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