Abstract. In multi-agent reinforcement learning systems, it is important to share a reward among all agents. We focus on the Rationality Theorem of Profit Sharing [5] and analyze ...
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...
As learning agents move from research labs to the real world, it is increasingly important that human users, including those without programming skills, be able to teach agents de...
A major challenge for traditional approaches to multiagent learning is to train teams that easily scale to include additional agents. The problem is that such approaches typically...
David B. D'Ambrosio, Joel Lehman, Sebastian Risi, ...
Ubiquitous Knowledge Discovery is a new research area at the intersection of machine learning and data mining with mobile and distributed systems. In this paper the main character...