Evolutionary online learning of cooperative behavior with situation-action pairs

We present a concept to use off-line learning approaches to achieve online learning of cooperative behavior of agents and instantiate this concept for evolutionary learning with agents based on prototype situation-action-pairs and the nearest-neighbor rule. For such an agent model also modeling of other agents can be achieved using the agent´s own architecture with situation-action-pairs derived from observations. We tested our online learning agents for different variants of the pursuit game and characterize the aspects of variants for which our online learning agents outperform off-line learning ones. Since our concept also allows a smooth transition from off-line learning to online learning and vice versa, the resulting system is able to win much more game variants than systems using either on- or off-line learning exclusively