The computation of agreements – or conventions- in agents’ societies via social learning methods, when agents aim to perform tasks in coordination with others is a challenge, especially when agents have to achieve multiple and conflicting goals simultaneously w.r.t. operational constraints, jointly with their peers, in settings with limited monitoring and interaction abilities.
In this line of research we investigate the use of multi-agent reinforcement learning methods to advance agents’ ability to act jointly, aiming to reaching equilibria or optima regarding their payoffs.
Big Data Analytics for Time Critical Mobility Forecasting: From raw data to trajectory-oriented mobility analytics in the aviation and maritime domains Book Forthcoming
Learning Conventions via Social Reinforcement Learning in Complex and Open Settings Proceeding
Sao Paulo, Brazil, 2017.
Multi-Agent Systems and Agreement Technologies – 13th European Conference, EUMAS 2015, and Third International Conference, AT 2015 Book
Springer, Athens, 2016, ISSN: ISBN 978-3-319-33508-7.
CoRR, abs/1503.07017 , 2015.
Fernando, Guttmann Christian Busquets Didac Koch (Ed.): Advances in Social Computing and Multiagent Systems, 541 , 2015.