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 / Using POMDP-based Reinforcement Learning for Online Optimization of Teaching Strategies in an Intelligent Tutoring System
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OGU.10214/7461 |
| Date | 05 September 2013 |
| Creators | Zhang, Pengfei |
| Contributors | Wang, Fangju |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis |
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