Travailler en équipe: le choix social appliqué au problème de la patrouille multi-agents

The multi-agent patrolling task constitutes a challenging issue for Artificial Intelligence and has the potential to cover a variety of domains ranging from agent-based simulations to crises management. In this article, we propose two decentralized, cooperative, auction-based strategies in which agents trade the nodes they have to visit. These strategies are inspired from the computational social choice theory and allow the agents to reason on the performances of the group rather than on their own. We show that these strategies perform at least as well as the state-of-the-art centralized performances, and better on specific criteria.