A framework for learning coordinated behavior

We sketch a framework for learning structured coordinated behavior, specifically the tactical behavior of Experimental Unmanned Vehicles (XUVs). We conceptualize an XUV unit as a multiagent system (MAS) on which we impose a command structure to yield a holarchy, a hierarchy of holons, where a holon is both a whole and a part. The formalism used is a conservative extension of Statecharts, called a Parts/whole Statechart, which introduces a coordinating whole as a concurrent component on a par with the coordinated parts; wholes are related to common knowledge. We use X-classifier systems (XCSs). Exploiting Statechart semantics, we translate Statechart transitions into classifiers and define data structures that interact with an XCS.