BEHAVIOR Explanation

Behavior explanation in artificial intelligence and robotics focuses on understanding and interpreting the actions of agents, both biological and artificial, to improve their design, control, and trustworthiness. Current research emphasizes developing models that link neural activity or agent actions to observable behaviors, often employing techniques like recurrent neural networks, transformers, and reinforcement learning algorithms, sometimes incorporating attention mechanisms and graph representations to capture complex dynamics. This work is crucial for enhancing the safety and reliability of autonomous systems, improving the interpretability of machine learning models, and furthering our understanding of biological behavior through computational modeling.