This paper introduces a multiagent reinforcement learning algorithm that converges with a given accuracy to stationary Nash equilibria in general-sum discounted stochastic games. ...
We present a novel paradigm for human to human asymmetric collaboration. There is a need for people at geographically separate locations to seamlessly collaborate in real time as ...
Ashutosh Morde, Jun Hou, S. Kicha Ganapathy, Carlo...
Distributed constraint satisfaction, in its most general acceptation, involves a collection of agents solving local constraint satisfaction subproblems, and a communication protoco...
In this paper, we first discuss the meaning of physical embodiment and the complexity of the environment in the context of multi-agent learning. We then propose a vision-based rei...
Abstract. In this paper, we review our Multi-Agent System (MAS) architecture (2LAMA) proposed to assist existing MAS. This architecture consists of two levels: the conventional MAS...