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dc.contributor.advisorDarken, Christian J.
dc.contributor.authorPapadopoulos, Sotirios
dc.date.accessioned2012-03-14T17:44:22Z
dc.date.available2012-03-14T17:44:22Z
dc.date.issued2010-09
dc.identifier.urihttp://hdl.handle.net/10945/5144
dc.descriptionThis thesis was done at the MOVES Institute
dc.description.abstractThe Cultural Geography (CG) model, under development in TRAC Monterey, is an open-source agent-based social simulation, designed to offer an insight into the response of the civilian population during Irregular Warfare (IW) operations. It implements social and behavioral science theories that govern the behaviors of agents within the simulation using Bayesian belief networks. At this stage, the agents within the CG model do not select their actions at all. Instead, all their actions are hard coded into the model's scenario file. As part of an attempt to improve the model, this effort sought to enhance the functionality within the model by exploring the use of utility functions and, more specifically, the concept of reinforcement learning. This study began with the development of a learning agent prototype. After the initial testing for its functionality, the code that was developed was inserted into the main CG model. Based on specially developed scenarios, and by employing a design of experiments methodology, we created experimental runs. By applying statistical and analysis techniques, we showed that reinforcement learning works properly inside the Social Network environment and produces the desired results. This study can be used as a starting point for the research of the effects of reinforcement learning in social modeling in general.en_US
dc.description.urihttp://archive.org/details/reinforcementlea109455144
dc.format.extentxvi, 53 p. : ill. ;en_US
dc.publisherMonterey, California. Naval Postgraduate Schoolen_US
dc.rightsThis publication is a work of the U.S. Government as defined in Title 17, United States Code, Section 101. Copyright protection is not available for this work in the United States.en_US
dc.subject.lcshModelingen_US
dc.subject.lcshVirtual realityen_US
dc.titleReinforcement learning a new approach for the cultural geography modelen_US
dc.typeThesisen_US
dc.contributor.secondreaderAlt, Jonathan
dc.contributor.corporateNaval Postgraduate School (U.S.)
dc.contributor.departmentComputer Science
dc.contributor.departmentModeling, Virtual Environment, and Simulation (MOVES)
dc.description.recognitionOutstanding Thesisen_US
dc.description.serviceHellenic Army authoren_US
dc.identifier.oclc671409965
etd.thesisdegree.nameM.S.en_US
etd.thesisdegree.levelMastersen_US
etd.thesisdegree.disciplineModeling, Virtual Environments, and Simulation Institute (MOVES)en_US
etd.thesisdegree.grantorNaval Postgraduate Schoolen_US
etd.verifiednoen_US
dc.description.distributionstatementApproved for public release; distribution is unlimited.


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