Case-Based Reasoning

Case-Based Reasoning (CBR) is an AI technique that utilizes previous experiences to solve new, similar problems. It follows these steps:

1. Retrieve: The CBR system identifies and retrieves relevant cases from its case library that resemble the current problem.
2. Reuse: The system then evaluates the applicability of the knowledge in these cases. This often involves adapting and adjusting the solutions to fit the current problem, as past resolutions may not be directly applicable.
3. Revise: After addressing the current problem, the revised solution is added to the case library. As more cases are solved and added, the CBR system becomes more adept at solving problems within its domain.

This adaptive, knowledge-driven approach makes CBR particularly useful in technical support, troubleshooting, and maintenance tasks.

One key advantage of Case-Based Reasoning (CBR) over Expert Systems and Business Rules Management Systems (BRMS) is that it doesn’t require the creation of explicit rules. Unlike Expert Systems, which need time-consuming rule formulation, CBR uses real-world cases and their solutions to address new problems. This significantly reduces the effort needed for knowledge engineering and makes CBR effective in areas where it’s difficult to codify expert knowledge into explicit rules. Additionally, CBR’s ability to learn from experience simplifies knowledge acquisition and enhances adaptability, making it valuable for handling constantly changing situations.

However, because CBR relies on past cases to solve problems, it can struggle with issues that lack close analogies in the case base. Moreover, as the case base grows, the process of storing and retrieving cases can become computationally intensive, potentially affecting the real-time performance of CBR systems.