Using POMDP-based Reinforcement Learning for Online Optimization of Teaching Strategies in an Intelligent Tutoring System

dc.contributor.advisorWang, Fangju
dc.contributor.authorZhang, Pengfei
dc.date.accessioned2013-09-05T18:55:20Z
dc.date.available2013-09-05T18:55:20Z
dc.date.copyright2013-08
dc.date.created2013-08-26
dc.date.issued2013-09-05
dc.degree.departmentSchool of Computer Scienceen_US
dc.degree.grantorUniversity of Guelphen_US
dc.degree.nameMaster of Scienceen_US
dc.degree.programmeComputer Scienceen_US
dc.descriptionUsing POMDP-based Reinforcement Learning for Online Optimization of Teaching Strategies in an Intelligent Tutoring Systemen_US
dc.description.abstractThis thesis is an investigation of "Using POMDP-based Reinforcement Learning for Online Optimization of Teaching Strategies in an Intelligent Tutoring System". A challenge in building an intelligent tutoring system (ITS) is to create and maintain an optimal teaching strategy. We cast an ITS as a partially observable Markov decision process (POMDP), and apply a reinforcement learning (RL) algorithm to learn the optimal teaching strategy through interactions between the system and the students. The optimal teaching strategy is chosen correctly and efficiently in tutoring a student, it is also learned and maintained in an online model. We present an RL algorithm based on POMDP for learning optimal teaching strategy, then describe the experiments and analyse the experimental results. The experiment has showed that the technique can remarkably improve an ITS's teaching performance.en_US
dc.identifier.urihttp://hdl.handle.net/10214/7461
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.subjectpartially observable Markov decision processen_US
dc.subjectreinforcement learning algorithmen_US
dc.subjectintelligent tutoring systemen_US
dc.subjectteaching strategiesen_US
dc.titleUsing POMDP-based Reinforcement Learning for Online Optimization of Teaching Strategies in an Intelligent Tutoring Systemen_US
dc.typeThesisen_US

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