Case-Based Reasoning (CBR)

Revision as of 11:30, 8 April 2023 by User (talk | contribs)
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)

Case-Based Reasoning (CBR) is an approach to problem-solving and decision-making in artificial intelligence (AI) that relies on the reuse of past experiences, called cases, to solve new problems. Instead of relying on general rules or algorithms to solve problems, CBR systems search a database of stored cases to find similar situations that have been previously encountered and adapt their solutions to address the current problem.

The core idea behind CBR is that similar problems tend to have similar solutions, so by learning from past experiences, a CBR system can find effective solutions to new problems more efficiently.

The Case-Based Reasoning process typically involves four main steps:

  • Retrieve: Given a new problem, the CBR system searches its case database to find one or more cases with similar characteristics or features to the current problem.
  • Reuse: The system adapts the solution from the retrieved cases to fit the current problem. This may involve modifying the solution, combining multiple solutions, or applying a solution as-is, depending on the degree of similarity between the cases.
  • Revise: The system tests the adapted solution to ensure its effectiveness in solving the current problem. If the solution is not effective, the system may iterate through the retrieval and reuse steps to find alternative solutions.
  • Retain: Once a successful solution has been found and applied, the system stores the new problem and its solution as a case in its database, to be used for future reference.

CBR has several advantages over other AI approaches, such as:

  • Flexibility: CBR systems can adapt to new situations and learn from experience, making them well-suited for domains with changing or incomplete knowledge.
  • Efficiency: By reusing past solutions, CBR systems can often find solutions more quickly than rule-based or algorithmic approaches, which may require extensive computation.
  • Transparency: CBR systems provide explanations for their solutions based on real-world cases, making their reasoning more understandable to humans compared to some other AI methods, like deep learning.
  • Robustness: Since CBR systems rely on actual experience rather than theoretical models, they can be more robust when dealing with noisy, incomplete, or uncertain data.

Case-Based Reasoning has been applied in various domains, such as medical diagnosis, legal reasoning, customer support, product recommendation, and design. However, it is important to note that CBR's effectiveness depends on the quality and representativeness of the case database and the ability to accurately measure similarity between cases. Additionally, CBR may not be well-suited for problems that require discovering entirely new solutions or where past experience is not applicable.

See Also