Bandwidth allocation for different service classes in heterogeneous wireless networks is an important issue for service provider in terms of balancing service quality and profit. I...
We control a population of interacting software agents. The agents have a strategy, and receive a payoff for executing that strategy. Unsuccessful agents become extinct. We investi...
Mechanism design (MD) has recently become a very popular approach in the design of distributed systems of autonomous agents. A key assumption required for the application of MD is...
This paper presents the dynamics of multi-agent reinforcement learning in multiple state problems. We extend previous work that formally modelled the relation between reinforcemen...
Functional magnetic resonance imaging (fMRI) has become increasingly used for studying functional integration of the brain. However, the large inter-subject variability in function...
— Achieving the Nash equilibria for single objective games is known to be a computationally difficult problem. However there is a special class of equilibria called evolutionary...
Although well understood in the single-agent framework, the use of traditional reinforcement learning (RL) algorithms in multi-agent systems (MAS) is not always justified. The fe...
Abstract. The application of reinforcement learning algorithms to multiagent domains may cause complex non-convergent dynamics. The replicator dynamics, commonly used in evolutiona...
Alessandro Lazaric, Jose Enrique Munoz de Cote, Fa...
This paper introduces a new model, i.e. state-coupled replicator dynamics, expanding the link between evolutionary game theory and multiagent reinforcement learning to multistate ...
We propose a novel method for functional segmentation of fMRI data that incorporates multiple functional attributes such as activation effects and functional connectivity, under a ...
Bernard Ng, Rafeef Abugharbieh, Martin J. McKeow...