A framework for learning coordinated behavior

Authors:
Albert Esterline;Chafic BouSaba;Abdollah Homaifar;Dan Rodgers
Affiliations:
Comp. Sci., N. Carolina A&T SU, Greensboro, NC;Elect. & Comp. Eng., NC A&T SU, Greensboro, NC;Elect. & Comp. Eng., NC A&T SU, Greensboro, NC;General Dynamics, Austin, TX
Venue:
GECCO '05 Proceedings of the 7th annual workshop on Genetic and evolutionary computation
Year:
2005

Citing 4
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Abstract

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.