This article summarizes the New Faculty Highlights talk with same title at AAAI 2021. Intelligent agents such as different types of robots will soon become an integral part our daily lives. In real-world multi-agent systems, most fundamental challenges are assigning tasks to multiple (task-level coordination problems) and planning collision-free paths for task locations (motion-level problems)....

We address model-free distributed stabilization of heterogeneous continuous-time linear multi-agent systems using reinforcement learning (RL). Two algorithms are developed. The first algorithm solves a centralized quadratic regulator (LQR) problem without knowing any initial stabilizing gain in advance. second builds upon the results algorithm, and extends it to with predefined interaction grap...

Multi-agent systems (MAS) are collections of autonomous computational entities (the agents) capable pro-actively pursuing goals and re-actively adapting to environment change. Agents in MAS exploit their social abilities, interacting with peers, situated capabilities as well, by perceiving acting on the world around them. From distributed computing intelligent systems, relevance agents software...

A multi-agent system consists of several computational entities capable autonomous actions, called agents, which communicate with each other, and have the ability to coordinate their actions cooperate. Multi-agent systems received a great interest attention over time, as they can be seen key enabling technology for complex applications, where distributed processing data, autonomy, high degree i...