A Novel Information Measure for Adaptive Controllers in Swarm Systems

In this work we have developed an information measure called maxcorr suitable for closed loop controllers that makes use of temporal unsupervised learning. It is novel because is computed at the input side of the controller and consider the semantic value of signals, rather then being based on the non semantic approach of Shannon's entropy. The maxcorr can be applied to individual agents to estimate their learning ability, but most importantly to social swarms where agents are learning all the time to achieve a common goal. Indeed in a social system all agents learn at the same time thus being unpredictable. However maxcorr quantitatively explains how agents of a social system select information to make the closed loop model more predictable. Results are compatible with the Luhmann's theory of social differentiation.