Neuro-Symbolic Learning for Context-Aware Real-Time Human–Agent Interaction

Author(s):

Vinod K. Mishra, Julian de Gortari Briseno, and Mani Srivastava

Abstract:

Robotic agents have been developed to perform a number of tasks under human command and sometimes independently as well. Human–agent teams can extend the capabilities of both humans and robots. Multi-Agent Reinforcement Learning (MARL) is a natural approach for such teams. The interaction within a team composed of human and robot agents considered so far ignores the role of physical context in MARL. Here, we present a distributed online MARL in such a scenario incorporating wireless communication and physical movement supplemented with intra-team communication. We present the system, its mathematical analysis, and some initial experimental results herein.