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

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dc.contributor.advisor Wang, Fangju
dc.contributor.author Zhang, Pengfei
dc.date.accessioned 2013-09-05T18:55:20Z
dc.date.available 2013-09-05T18:55:20Z
dc.date.copyright 2013-08
dc.date.created 2013-08-26
dc.date.issued 2013-09-05
dc.identifier.uri http://hdl.handle.net/10214/7461
dc.description Using POMDP-based Reinforcement Learning for Online Optimization of Teaching Strategies in an Intelligent Tutoring System en_US
dc.description.abstract This 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 e ciently 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.language.iso en en_US
dc.subject Pengfei Zhang MSc Thesis 2013 en_US
dc.title Using POMDP-based Reinforcement Learning for Online Optimization of Teaching Strategies in an Intelligent Tutoring System en_US
dc.type Thesis en_US
dc.degree.programme Computer Science en_US
dc.degree.name Master of Science en_US
dc.degree.department School of Computer Science en_US
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