Homeokinesis --  Emergent behavior for autonomous agents.

       Ralf Der

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Can the self-organization of behavior be more than wishful thinking? From our experiments with real robots we have learned that true self-organization requires a driving force which must be rooted in the agent itself.  A useful example of this thinking is given by the principle of homeostasis according to which the behavior of an agent in the world arises from its desire to keep internally an equilibirum state.
We introduce a new principle - homeokinesis - which is completely unspecific and yet induces specific, seemingly goal-oriented behaviors of an agent in a complex external world. We view homeokinesis as the dynamical pendant of homeostasis. Like the latter it derives behavior from an entirely internal perspective so that coarsely speaking behavior emerges as a by-product of satisfying the internal needs of the agent. We give this general aim a constructive formulation in the following way. We suppose that the agent is equipped with an adaptive model of its behavior. A learning signal for both the model and the controller is derived from the misfit between the real behavior of the agent in the world and that predicted by the model. As we could show with several examples this misfit is minimized if the agent exhibits a smooth, controlled behavior.
In this way, a learning signal for the adaptation of the behavior is derived from a purely internal perspective. However we have found that using a predictive model will  lead to active behavior modes only if the robot is  given a drive for activity from outside. When using a retrospective model the system is found to develop this drive for activity by itself due to spontaneous symmetry breaking mechanisms known from the physics of self-organizing systems. The reason is that the model now generates a dynamics backward in time leading to fluctuation amplification which is a necessary prerequisit for self-organization.

The kind of behavior learned by the agent depends on the structural complexity of the behavior model. Different models generate different modes of behavior. This may be heplful in artificial evolution in that by modifying the structural complexity of the behavior model evolution may play directly with complex behavior modes  which are already more or less ''reasonable'' in the world.

Recent papers and videos may be found here. 


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