Toward optimal pedagogical action patterns by means of Partially Observable Markov Decision Process

Manuel Mejía-Lavalle, Hermilo Victorio, Alicia Martínez, Grigori Sidorov, Enrique Sucar, Obdulia Pichardo-Lagunas

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

Good pedagogical actions are key components in all learning-teaching schemes. Automate that is an important Intelligent Tutoring Systems objective. We propose apply Partially Observable Markov Decision Process (POMDP) in order to obtain automatic and optimal pedagogical recommended action patterns in benefit of human students, in the context of Intelligent Tutoring System. To achieve that goal, we need previously create an efficient POMDP solver framework with the ability to work with real world tutoring cases. At present time, there are several Web available POMDP open tool solvers, but their capacity is limited, as experiments showed in this paper exhibit. In this work, we describe and discuss several design ideas toward obtain an efficient POMDP solver, useful in our problem domain.

Idioma originalInglés
Título de la publicación alojadaAdvances in Soft Computing - 15th Mexican International Conference on Artificial Intelligence, MICAI 2016, Proceedings
EditoresObdulia Pichardo-Lagunas, Sabino Miranda-Jimenez
EditorialSpringer Verlag
Páginas473-480
Número de páginas8
ISBN (versión impresa)9783319624273
DOI
EstadoPublicada - 2017
Evento15th Mexican International Conference on Artificial Intelligence, MICAI 2016 - Cancun, México
Duración: 23 oct. 201628 oct. 2016

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen10062 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia15th Mexican International Conference on Artificial Intelligence, MICAI 2016
País/TerritorioMéxico
CiudadCancun
Período23/10/1628/10/16

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