Safe Agent

Safe agent research focuses on designing artificial intelligence agents that reliably and ethically perform tasks in real-world environments, mitigating risks such as adversarial attacks, bias, and unintended consequences. Current research emphasizes developing robust safety architectures, including input-output filters, safety agents, and hierarchical systems, often employing reinforcement learning algorithms like actor-critic methods and particle filters to achieve stability and optimize performance while adhering to safety constraints. This field is crucial for responsible AI deployment across various sectors, from autonomous vehicles to human-AI collaboration, ensuring both efficacy and safety in increasingly complex applications.