Interactive Behavior

Interactive behavior research focuses on understanding and modeling how agents, whether human or artificial, interact and influence each other's actions over time. Current research emphasizes developing robust models for multi-turn interactions, particularly within the context of large language models (LLMs), using techniques like reinforcement learning from human feedback and novel architectures inspired by cognitive neuroscience. This work is crucial for improving human-AI collaboration, advancing autonomous systems, and gaining insights into human social dynamics, with applications ranging from assistive robotics to more effective educational tools. Furthermore, understanding interactive behavior is vital for designing safe and reliable AI systems that can operate effectively in complex, dynamic environments.