Multi-agent learning is a crucial method to control or find solutions for systems, in which more than one entity needs to be adaptive. In today's interconnected world, such s...
Many applications in multiagent learning are essentially convex optimization problems in which agents have only limited communication and partial information about the function be...
Renato L. G. Cavalcante, Alex Rogers, Nicholas R. ...
In this paper, we propose a Quantified Distributed Constraint Optimization problem (QDCOP) that extends the framework of Distributed Constraint Optimization problems (DCOPs). DCOP...
Virtual Actors are at the heart of Interactive Storytelling systems and in recent years multiple approaches have been described to specify their autonomous behaviour. One well kno...
Bayesian games can be used to model single-shot decision problems in which agents only possess incomplete information about other agents, and hence are important for multiagent co...
Frans A. Oliehoek, Matthijs T. J. Spaan, Jilles St...