Evolving agent societies through imitation controlled by artificial emotions

Abstract

An architecture is proposed that combines a simple learning method with one of the most natural evaluation systems: imitation controlled by emotions. Using this architecture agents develop behavioral clusters and form a society that improves its ability to reach a given goal over time. Imitation works by observing and applying behavior sequences (episodes). This leads to new and diverse episodes, because the observation introduces small errors. On the other hand, bad episodes are forgotten if they don't help the agents to satisfy their emotional system that plays the role of an inherent performance measurement. After a while, the agents can be grouped by their typical behavioral patterns. Since these imitated sequences can be seen as "memes" similar to genes in the biological world, this paper explores imitation from the view of memetic proliferation. We show by simulation that using imitation combined with emotions as evaluation measure tasks can be performed by an agent society without having to specify them in detail. The society's performance is quantified using an entropy measure to qualitatively evaluate the emerging behavioral clusters. © Springer-Verlag Berlin Heidelberg 2005.