What is Case-Based Reasoning
An approach in which a system solves a new problem by finding similar past cases and adapting their solutions.
Definition
Case-Based Reasoning is an approach in which a system solves a new problem by finding similar past cases and adapting their solutions. Simply put, this concept helps to understand how AI makes decisions, constructs reasoning, or models complex systems. In practice, it helps to understand what capabilities the tool actually has, what data it will need, and what limitations are worth checking before implementation.
Example
The support service looks for similar customer requests and offers the operator a solution that has already worked before.
Why it matters
The method is understandable to users and useful where experience is accumulated in the form of cases. This helps you choose AI tools not by big promises, but by how they work in a real problem.
How it works
The approach describes a problem as a set of states, knowledge, probabilities, or rules, after which the system selects an action, output, or prediction. In the case of the term “Reasoning from analogous cases”, it is important to look separately at the data, quality criteria and application conditions.
Where it is used
- Used in expert systems, planning, robots, simulations, intelligent assistants and scientific models.
Limitations
The limitation is that the formal model simplifies reality: the conclusion may look convincing but depend on incomplete rules or data.