Learning acceptable windows of contingency
By learning a range of possible times over which the effect of an action can take place, a robot can reason more effectively about causal and contingent relationships in the world. An algorithm is presented for learning the interval [t(1min), t(1max)] of possible times during which a response to an action can take place. The algorithm was implemented on a physical robot for the domains of visual self-recognition and auditory social-partner recognition. The environment model assumes that natural environments generate Poisson distributions of random events at all scales. A linear-time algorithm called Poisson threshold learning can generate a threshold T that provides an arbitrarily small rate of background events lambda(T), if such a threshold exists for the specified error rate.
contingency causality self-recognition social-partner recognition poisson threshold learning developmental robotics