Starting from a given one-shot game played by a finite population of agents living in flatline, a circular or constrained grid structured by the classical definitions of neighborh...
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 order for robotic systems to be successful in domains with other agents possibly interfering with the accomplishing of goals, the agents must be able to adapt to the opponents...
To avoid the curse of dimensionality, function approximators are used in reinforcement learning to learn value functions for individual states. In order to make better use of comp...
Recently, adaptive course generation has been focusing by several researchers. We have built ACGs system to create adaptive courses for each learner based on evaluating demand, ab...