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.
Specific Topics
- Reaching semantic agreements in large-scale multi-agent systems
- Computing emerging conventions in large scale multi-agent systems
Recent Publications
2015 |
~n, Carlos Iván Ches; Onaindia, Eva; Ossowski, Sascha; Vouros, George A Special Issue on Agreement Technologies Journal Article Information Systems Frontiers Journal, Springer, 2015. @article{302b, title = {Special Issue on Agreement Technologies}, author = {Carlos Iván Ches ~n and Eva Onaindia and Sascha Ossowski and George A Vouros}, doi = {10.1007/s10796-015-9584-z}, year = {2015}, date = {2015-01-01}, journal = {Information Systems Frontiers Journal, Springer}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
2014 |
Vouros, George Decentralized Semantic Coordination of Interconnected Entities via Belief Propagation Journal Article AI Communications, On Line , 2014, ISSN: 1875-8452. @article{293b, title = {Decentralized Semantic Coordination of Interconnected Entities via Belief Propagation}, author = {George Vouros}, url = {http://iospress.metapress.com/content/l4x5730110004278/}, doi = {10.3233/AIC-140624}, issn = {1875-8452}, year = {2014}, date = {2014-01-01}, journal = {AI Communications}, volume = {On Line}, keywords = {}, pubstate = {published}, tppubtype = {article} } |