It is known that the complexity of the reinforcement learning algorithms, such as Q-learning, may be exponential in the number of environment’s states. It was shown, however, th...
Classically, an approach to the multiagent policy learning supposed that the agents, via interactions and/or by using preliminary knowledge about the reward functions of all playe...
Running several sub-optimal algorithms and choosing the optimal one is a common procedure in computer science, most notably in the design of approximation algorithms. This paper d...
We develop a Belief-Desire-Intention (BDI) style agent-oriented programming language with special emphasis on the semantics of goals in the presence of the typical BDI failure han...
Coalition formation is an important form of interaction in multiagent systems. It enables the agents to satisfy tasks that they would otherwise be unable to perform, or would perf...
We present a multi-agent coordination technique to maintain throughput of a large-scale agent network system in the face of failures of agents. Failures do not just deteriorate th...
The paper deals with on-board planning for a satellite swarm via communication and negotiation. We aim at defining individual behaviours that result in a global behaviour that me...
Since about two decades, many works have been made in order to provide solutions to the vehicle platoon problem. The main issue related to platoon systems consists in controling t...
Jean-Michel Contet, Franck Gechter, Pablo Gruer, A...