Multi-agent systems can be used to address problems in a variety of domains, including
robotics, distributed control, telecommunications, and economics. The complexity of many …

Multiagent systems are rapidly finding applications in a variety of domains, including
robotics, distributed control, telecommunications, and economics. The complexity of many …

The last two decades have seen a clear trend toward crafting intelligent vehicles based on
the significant advances in communication and computing paradigms, which provide a safer …

In this article we describe a set of scalable techniques for learning the behavior of a group of
agents in a collaborative multiagent setting. As a basis we use the framework of coordination …

The paper develops QD-learning, a distributed version of reinforcement Q-learning, for multi-
agent Markov decision processes (MDPs); the agents have no prior information on the …

Planning under uncertainty faces a scalability problem when considering multi-robot teams,
as the information space scales exponentially with the number of robots. To address this …

Multi-agent systems are rapidly finding applications in a variety of domains, including
robotics, distributed control, telecommunications, economics. Many tasks arising in these …

Coordination graphs offer a tractable framework for cooperative multiagent decision making
by decomposing the global payoff function into a sum of local terms. Each agent can in …

We use a decomposition approach to generate cooperative strategies for a class of multi-
vehicle control problems. By introducing a set of tasks to be completed by the team of …

We describe Utile Coordination, an algorithm that allows a multiagent system to learn where
and how to coordinate. The method starts with uncoordinated learners and maintains …