DEDALE

Team : SMA

Dedale aims to facilitate and improve the experimental evaluation conditions of MAS algorithms and to contribute to the progress of the field towards decentralised solutions able to deal with real-world situations. Dedale is dedicated to the study of multi-agents coordination, learning and decision-making problems under real-life hypotheses. Dedale offers open, dynamic, asynchronous and partially observable environments. It allows to tackle either cooperative or competitive exploration, patrolling, pickup and delivery, treasure(s) or agent(s) hunt problems with teams from one to dozens of heterogeneous agents in discrete or continuous environments. These strengths make Dedale able to become a unifying environment for both MAS research and teaching communities in their goal to work and evaluate their proposals under realistic hypotheses.

Software leader : Cédric Herpson
https://dedale.gitlab.io/