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The Complex Process of Scaling the Integration of Technology Enhanced Learning in Mainstream Classrooms Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Minimizing Computing Costs of Policy Trees in a POMDP-based Intelligent Tutoring System

Author : Fangju Wang

Published in: Computers Supported Education

Publisher: Springer International Publishing

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Abstract

Uncertainties exist in intelligent tutoring. The partially observable Markov decision process (POMDP) model may provide useful tools for handling uncertainties. The model may enable an intelligent tutoring system (ITS) to choose optimal actions when uncertainties occur. A major difficulty in applying the POMDP model to intelligent tutoring is its computational complexity. Typically, when a technique of policy trees is used, in making a decision the number of policy trees to evaluate is exponential, and the cost of evaluating a tree is also exponential. To overcome the difficulty, we develop a new technique of policy trees, based on the features of tutoring processes. The technique can minimize the number of policy trees to evaluate in making a decision, and minimize the costs of evaluating individual trees.

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Appendix
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Metadata
Title
Minimizing Computing Costs of Policy Trees in a POMDP-based Intelligent Tutoring System
Author
Fangju Wang
Copyright Year
2017
Book
Computers Supported Education

Print ISBN: 978-3-319-63183-7

Electronic ISBN: 978-3-319-63184-4

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-63184-4_9

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