Dedale

Dedale

Dedale is the first simulator dedicated to the study of multi-agent coordination, learning and decision-making problems under realistic hypotheses. Highly parametrizable, Dedale allows to tackle either cooperative or competitive exploration, patrolling, treasure(s) or agent(s) hunt problems with teams from one to dozens of heterogeneous agents in discrete or continuous environments, local or distributed over the network.

Dedale aims to facilitate and improve the experimental evaluation conditions of the developed MAS algorithms, and to contribute to the progress of the field towards decentralised solutions able to deal with real-world situations.

Cédric HERPSON
Cédric HERPSON
Associate Professor of Artificial Intelligence

My research interests include long-term autonomy, coordination, learning and decision making.