Adaptive Resonance Theory

Adaptive Resonance Theory (ART) is a process in artificial intelligence that helps computers recognize patterns and learn new information without forgetting what they already know. It works by matching incoming data to existing categories; if the match is good enough, the system reinforces the pattern. If not, it creates a new category. This approach allows continuous learning and adaptation, much like how the brain can recognize new objects while maintaining knowledge of familiar ones. ART balances stability (retaining known patterns) and plasticity (learning new ones), making it effective for tasks like image recognition and speech processing.