Imitation, Maslow´s Pyramid and Multi-Agent Systems

We review an interesting article published by Le Guen Herve and Moga Sorin in Proceedings of International Joint Conference on Neural Networks (2009).

The authors study the influence of imitation within a population of artificial agents following Maslow´s Pyramid of needs. Imitation is the possible origin of communication and social learning. The imitative abilities include many types like facial imitation, perception by the infant that he or she is being imitated and empathy. The interest in empathy induces the study of emotions which Herve and Sorin link to the analysis of social needs as modeled by the Maslow´s Pyramid of motivations. Although the influence of each particular need varies from one person to another the principle is there are two main classes of needs. The most important needs are called primary needs (eating, breathing, etc.) while the second class of needs constitute the social aspect of human motivation and includes the need for esteem. This need is bidirectional and has been interpreted by the authors as the need to imitate and the need to be imitated. As the fact of being imitated is perceived by the agent concerned and as imitation is likely to generate empathic satisfaction, the authors model them as expected rewards. Based on a model of an autonomous robot with goal-oriented navigation and imitation capabilities, the robot goals are derived from internal variables that have to be maintained in a comfort area. The values of these variables decrease in time. The robot population´s task is to explore an unknown environment and to localize sources corresponding to its needs. Its survival will depend upon the satisfaction of these needs by discovering the different locations of the sources. The behaviors are obstacle avoidance, goal-oriented navigation, imitation and exploration. The emotional signature expresses the current state and the expected state of the agent: an agent in its comfort area displays a neutral signature whereas an internal variable below a certain threshold induces pain. That pain will cause a potential empathic response that is likely to incite another agent to move toward a known source. Besides a motivated agent is likely to provoke an attractive empathic response: keeping a motivated agent in its own field of perception becomes a source of motivation. It leads to the selection of the imitation target according to its motivational state.

The results of the simulation with Maslow agents showed an enhancement of the global survival rate even with a very small population wherein communications are not frequent.

The authors have proposed a holistic approach to the implementation of imitation in autonomous agents.

We recommend this excellent article because its ideas permit to build a simple and scalable model of agent useful in applications such as networks or swarm piloting.