CASE-BASED REASONING SYSTEM
Abstract
A Case-Based Reasoning (CBR) system is an intelligent problem-solving
approach in the field of Artificial Intelligence that uses past experiences
(cases) to solve new problems. Instead of relying solely on predefined
rules or models, a CBR system retrieves similar previously solved cases
and adapts their solutions to fit the current situation. This method mimics
human reasoning, where decisions are often based on past experiences.
The core processes of a CBR system include case retrieval, case reuse,
case revision, and case retention. During retrieval, the system searches a
case database for similar past problems. In reuse, it applies the solutions
from those cases to the new problem. The revision stage involves testing
and modifying the proposed solution if necessary, while retention stores
the new experience for future use.
CBR systems are widely applied in areas such as medical diagnosis,
customer support, fault detection, and legal decision-making. They offer
advantages such as continuous learning, adaptability, and ease of
knowledge acquisition. However, their effectiveness depends on the
quality and organization of the case database.
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