Danassis, PanayiotisFaltings, Boi2019-08-142019-08-142019-08-142019https://infoscience.epfl.ch/handle/20.500.14299/159817WOS:000474345000079We investigate the problem of multi-agent coordination under rationality constraints. Specifically, role allocation, task assignment, resource allocation, etc. Inspired by human behavior, we propose a framework (CA^3NONY) that enables fast convergence to efficient and fair allocations based on a simple convention of courtesy. We prove that following such convention induces a strategy which constitutes an epsilon-subgame-perfect equilibrium of the repeated allocation game with discounting. Simulation results highlight the effectiveness of CA^3NONY as compared to state-of-the-art bandit algorithms, since it achieves more than two orders of magnitude faster convergence, higher efficiency, fairness, and average payoff.[learning and adaptation] multiagent learning[economic paradigms] noncooperative games: theory & analysisanti-coordinationtext::conference output::conference proceedings::conference paper