A stochastic graph game is played by two players on a game graph with probabilistic transitions. We consider stochastic graph games with -regular winning conditions specified as Ra...
Decentralized Markov Decision Processes are a powerful general model of decentralized, cooperative multi-agent problem solving. The high complexity of the general problem leads to...
We address the problem of coordinating the plans and schedules for a team of agents in an uncertain and dynamic environment. Bounded rationality, bounded communication, subjectivi...
Reinforcement Learning (RL) is a simulation-based technique useful in solving Markov decision processes if their transition probabilities are not easily obtainable or if the probl...
Resources consumption control is crucial in the autonomous rover context. Most of the time, the resources consumption is probabilistic. During execution time, the rover has to adap...
Simon Le Gloannec, Abdel-Illah Mouaddib, Fran&cced...