For successful coordination and cooperation in a multiagent system, participants need to establish a sufficiently accurate awareness of the current situation. Awareness is underst...
Ontologies play a key role in agent communication and the emerging Semantic Web. Axioms are an important component of ontologies to describe the relationships among the concepts. ...
Distributing scarce resources among agents in a way that maximizes the social welfare of the group is a computationally hard problem when the value of a resource bundle is not lin...
Factored representations, model-based learning, and hierarchies are well-studied techniques for improving the learning efficiency of reinforcement-learning algorithms in large-sca...
Carlos Diuk, Alexander L. Strehl, Michael L. Littm...
In many Multi-Agent Systems (MAS), agents (even if selfinterested) need to cooperate in order to maximize their own utilities. Most of the multi-agent learning algorithms focus on...
Jose Enrique Munoz de Cote, Alessandro Lazaric, Ma...
We present a technique for reducing a normal-form (aka. (bi)matrix) game, O, to a smaller normal-form game, R, for the purpose of computing a Nash equilibrium. This is done by com...
The VCG mechanism is the canonical method for motivating bidders in combinatorial auctions and exchanges to bid truthfully. We study two related problems concerning the VCG mechan...
In this paper, we propose a new integration approach for simulation and behaviour in the learning context that is able to coherently manage the shared virtual environment for the ...
Communication in open heterogeneous multi agent systems is hampered by lack of shared ontologies. To overcome these problems, we propose a layered communication protocol which inc...
Jurriaan van Diggelen, Robbert-Jan Beun, Frank Dig...
RVRL (Rule Value Reinforcement Learning) is a new algorithm which extends an existing learning framework that models the environment of a situated agent using a probabilistic rule...