Multi-agent Collaborative AI

This challenge addresses the fact that in many real-life situations, different autonomous decision-making entities are not standalone, but interact with each other. This requires flexible coordination mechanisms for these autonomous decision-making entities, allowing to adapt to their environments, to interact flawlessly with humans, and to exchange privacy-sensitive data, in order to solve collaborative tasks in a highly connected and rapidly changing world.

AI systems that interact autonomously with other decision-making entities 

Icon of Multi-agent Collaborative AI

The research in this grand challenge is focused on multi-agent systems. In multi-agent systems, it is fundamental that each agent has its own goals and intentions and none of the agents knows the entire system, nor has direct control over the other agents. Multi-agent systems can be found in many domains, such as trading systems, network routing systems, intelligent traffic systems, smart grids, autonomous vehicles, internet of things, collaborating robots and machines, and many more. The research focuses on the adaptivity, robustness, manageability of multi-agent systems and how to formulate guarantees on their proper functioning.

Structure of the challenge

Overview of GC3

Work Packages

WP 1 Use Cases

WP 2 Multi-Agent Control Systems

WP 3 Hybrid Multi-Agent Systems for Collective Action

WP 4 Distributed Data Intelligence


Multiple research groups collaborate on this research domain. This table mentions the contact person and his/her affiliation.

Ann Nowé

Management team, VUB AI Lab

Leander Schietgat

Management team & WP1 Lead: Use Cases, VUB AI Lab

Bram Vanderborght

WP2 Lead: Multi-Agent Control Systems, VUB Robotics & Multibody Mechanics

Bart de Boer

WP3 Hybrid Multi-Agent Systems for Collective Action, VUB AI Lab

Jan Van den Bussche

WP4 Lead: Distributed Data Intelligence, UHasselt