NDLI: Learning coordinated behavior: XCSs and statecharts

Content Provider	IEEE Xplore Digital Library
Author	Bou-Saba, C.W. Esterline, A. Homaifar, A. Rodgers, D.
Copyright Year	2005
Description	Author affiliation: Dept. of Electr. & Comput. Eng., North Carolina A & T State Univ., Greensboro, NC, USA (Bou-Saba, C.W.)
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), 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.
Starting Page	436
Ending Page	442
File Size	249921
Page Count	7
File Format	PDF
ISBN	0780392981
DOI	10.1109/ICSMC.2005.1571185
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2005-10-12
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multiagent systems Genetic algorithms Data structures Research and development Vehicle dynamics Command and control systems Encapsulation Learning Autonomous agents Communication system control Holon X classifier systems Statechart semantics Coordination Tactical behavior
Content Type	Text
Resource Type	Article

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