It is found in highly social species which show, from a human observer point of view, 'intelligent' behavior and traits supporting the evolution of traditions and culture. Recently, imitation has begun to be studied in domains dealing with such non-natural agents as robots, and as a tool for easing the programming of complex tasks or endowing groups of robotic agents with the ability to share skills without the intervention of a programmer. Imitation plays an important role in the more general context of interaction and collaboration between agents and humans, e.g. between software agents and human users. Intelligent software agents need to get to know their users in order to assist them and do productive work on behalf of humans. Imitation is therefore a means of establishing a 'social relationship' and learning about the actions of the user, in order include them into an agent's own behavioral repertoire.
Imitation is on the one hand considered as an efficient mechanism of social learning.
On the other hand, imitation methods as in programming by demonstration setups in robotics and machine learning have primarily focused on the technological dimensions, while disregarding the more social and developmental functions. Additionally, the split between imitation research in natural sciences and the sciences of the artificial has been difficult to bridge, as we lack a common framework supporting an interdisciplinary approach. Imitation involves at least two agents sharing a context, allowing one agent to learn from the other. Thus, learning by imitation and learning to imitate are not just a specific topics in machine learning, but can be seen as a benchmark challenges for successful real-world AI Systems. All submissions will be peer-reviewed.