Learning coordinated behavior: XCSs and statecharts

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), where learning acquires a population of weighted condition-action classifier rules that direct behavior. Environmental rewards modify classifier strength, and a genetic algorithm (GA) modifies the classifier population. Exploiting statechart semantics, we translate statechart transitions into classifiers and define data structures that interact with an XCS. Difficulties arise in learning wholes and where the GA makes structural changes.