We study the evolution of social behaviors within a behavioral framework. To this end, we define a minimal social situation that is experimented with both humans and simulations based on reinforcement learning algorithms. We analyse the dynamics of behaviors in this situation by way of operant conditioning. We show that the best reinforcement algorithm, based on Staddon-Zhang's equations, has a performance and a variety of behaviors that comes close to that of humans, and clearly outperforms the well-known Q-learning. Though we use here a rather simple, yet rich, situation, we argue that operant conditioning deserves much study in the realm of artificial life, being too often misunderstood, and confused with classical conditioning.
['Samuel Delepoulle', 'Philippe Preux', 'Jean-Claude Darcheville']
Evolution of cooperation within a behavior-based perspective : Confronting nature and animats