Analysis of Performance of Cognitive Agents Learning to Cross a CA Based Highway using Improved Learning Mechanism

dc.contributor.advisorLawniczak, Anna Jr
dc.contributor.authorYu, Fei
dc.date.accessioned2016-08-11T13:00:44Z
dc.date.available2016-08-11T13:00:44Z
dc.date.copyright2016-08
dc.date.created2016-08-03
dc.date.issued2016-08-11
dc.degree.departmentDepartment of Mathematics and Statisticsen_US
dc.degree.grantorUniversity of Guelphen_US
dc.degree.nameMaster of Scienceen_US
dc.degree.programmeMathematics and Statisticsen_US
dc.description.abstractThis thesis presents a modification of a simulation model of cognitive agents learning to cross safely a cellular automaton based highway. Analysis and reflection focusing on the design of the initial simulation model, the mechanism of “learning” used in this model, and the shortcomings of the model are described. Based on the analysis of the simulation results of the initial model, a modification of the learning mechanism is proposed, implemented and performance of the modified simulation model is investigated. A large amount of simulation data is generated by the modified simulation model for various combinations of configuration parameters’ values. An exhaustive comparison analysis of simulation results produced by the modified and original models is conducted. These analysis involves the design of simulation experiments, data visualization, analysis of various statistics as well as the systematic comparison of clustering of histogram plots of these models. This comparative analysis of models performance conducted for various combinations of parameters brings a better understanding of the performance of cognitive agents’ models, in particular the agents decision-making processes, as well as their learning mechanisms. The thesis provides recommendations on how to improve further the simulation model of cognitive agents learning to cross a highway, their decision-making formula and the design of knowledge-based table.en_US
dc.identifier.urihttp://hdl.handle.net/10214/9857
dc.language.isoenen_US
dc.publisherUniversity of Guelphen_US
dc.rights.licenseAll items in the Atrium are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectcognitive agentsen_US
dc.subjectmachine learningen_US
dc.subjectcellular automaton based highwayen_US
dc.titleAnalysis of Performance of Cognitive Agents Learning to Cross a CA Based Highway using Improved Learning Mechanismen_US
dc.typeThesisen_US

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