Human and AI agents Collaboration in High Dynamic Environment
The main focus of this talk is "human-AI teaming", specifically the mode of "human-AI collaboration" where humans and AI agents accomplish tasks together in a complex system. Therefore the objective cannot be achieved by just alone human or agent, and the responsibilities in the environment are partitioned and/or shared between humans and agents. Collaborative multi-agent reinforcement learning (MARL) as a specific category of reinforcement learning (RL) provides effective results with agents learning from their observations, received rewards, and internal interactions between agents. However, centralized learning methods with a joint global policy in a highly dynamic environment present unique challenges in dealing with large amounts of information. This study proposes innovative solutions to address the complexities of a collaboration between human and RL agents where the goals pursued cannot be achieved by a human alone or agents alone.
Dr. Neda Navidi is an expert AI researcher with more than fifteen years of experience in designing and developing optimization systems, signal processing, practical AI, and theoretical ML/DL/RL algorithms. Neda has leveraged her extensive experience to harness the potential of new technologies and implement them across industrial solutions and services related to human-AI collaboration. She has also been a guest lecturer at the Quebec University of Montreal. She has more than 30 scientific papers in different journals and conferences. Dr. Neda holds a Ph.D. in AI (autonomous driving field) from École de Technologie Supérieure (ÉTS), and postdoctoral from HEC Montréal, McGill University, and Polytechnique Montréal.