Predictive information and emergent cooperativity in a chain of mobile robots

Measures of complexity are of immediate interest for the field of autonomous robots both as a means to classify the behavior and as an objective function for the autonomous development of robot behavior. In the present paper we consider predictive information in sensor space as a measure for the behavioral complexity of a chain of two-wheel robots which are passively coupled and controlled by a closed-loop reactive controller for each of the individual robots. The predictive information, the mutual information between the past and the future of a time series, is approximated by restricting the time horizons to a single time step. This is exact for Markovian systems but seems to work well also for our robotic system which is strongly non-Markovian.When in a maze with many obstacles, the approximated predictive information of the sensor values of an individual robot is found to have a clear maximum for a controller which realizes the spontaneous cooperation of the robots in the chain so that large areas of the maze can be visited.

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