# Behavioral Adaptation in AI ## Definition/Description Behavioral adaptation in AI refers to the ability of artificial systems to modify their responses, strategies, or operations based on past experiences, environmental changes, or learned patterns. ## Key Points - Enables dynamic improvement in decision-making and user interaction. - Relies on mechanisms like memory integration, symbolic processing, and reinforcement learning. - Can involve subconscious processes such as dreaming or symbolic theme extraction. ## Connections - Related notes: [[Incorporating Dream Insights into Conscious Processing]], [[Artificial Cognition]] - Broader topics: [[Adaptive Systems in AI]], [[Learning Mechanisms in AI]] ## Questions/Reflections - How can behavioral adaptation be balanced to prevent overfitting to specific patterns? - What metrics best evaluate the success of adaptive behaviors in AI systems? ## References (Add source links, citations, or related resources.)