Sensorimotor action sequence learning with application to face recognition under discourse

Our goal is to enable machines to learn directly from sensory input streams. The learning machine does not require human teacher to specify any content-level rule. Such a capability requires a fundamentally new way of addressing the learning problem, one that unifies learning and performance phases and requires a systematic self-organization capability. The presented approach enables the system to self-organize its internal representation, and uses a systematic way to automatically build multi-level representation. In the experiments presented, we study the behavior of the method for automatic state self-organization and automatic level building that involves two levels. We test the algorithm for the problem of face recognition under a simple but important discourse scenario-a primary mode of our goal for human-machine interactive learning