In this paper, we investigate Reinforcement learning (RL) in multi-agent systems (MAS) from an evolutionary dynamical perspective. Typical for a MAS is that the environment is not ...
Karl Tuyls, Pieter Jan't Hoen, Bram Vanschoenwinke...
Developing multi-agent simulations seems to be rather straight forward, as active entities in the original correspond to active agents in the model. Thus plausible behaviors can be...
Which agent architectures are capable of justifying descriptions in terms of the `higher level' mental concepts applicable to human beings? We propose a new kind of architect...
Multiuser diversity (MUDiv) is one of the central concepts in multiuser (MU) systems. In particular, MUDiv allows for scheduling among users in order to eliminate the negative effe...
Fault-tolerant behavior is an important non-functional requirement for systems that involve high criticality. We present a framework which allows the analysis of faulttolerant beh...