An Behavior-based Robotics
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Imitation in animals and artifacts
Behavioral diversity in learning robot teams
Behavioral diversity in learning robot teams
MEXI: machine with emotionally eXtended intelligence
Design and application of hybrid intelligent systems
Adaptivity at every layer: a modular approach for evolving societies of learning autonomous systems
Proceedings of the 2008 international workshop on Software engineering for adaptive and self-managing systems
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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.