Tutoring and multi-agent systems: Modeling from experiences

Tutoring systems become complex and are offering varieties of pedagogical software as course modules, exercises, simulators, systems online or offline, for single user or multi-user. This complexity motivates new forms and approaches to the design and the modelling. Studies and re- search in this field introduce emergent concepts that allow the tutoring system to interact efficiently with potential users, by enhancing ergonomic service, performing response time and allowing bet- ter adaptability. The introduction of concepts such as multi-agent systems (MAS) allowed web technology to improve the process of modeling and designing for distance learning, and thus offer convincing solutions. The presentation of some relevant projects that associate MAS to the Web may highlight the benefits of this association in an innovative way.

[1]  Ann Nowé,et al.  Evolutionary game theory and multi-agent reinforcement learning , 2005, The Knowledge Engineering Review.

[2]  Lynn Nadel,et al.  Encyclopedia of Cognitive Science , 2003 .

[3]  R. Bellman.  A Markovian Decision Process , 1957 .

[4]  Abraham Wald,et al.  Statistical Decision Functions , 1951 .

[5]  Adam Ledgeway.  Printed in the United Kingdom , 2008 .

[6]  Clara-Inés Peña,et al.  Intelligent Agents in a Teaching and Learning Environment on the Web , 2002 .

[7]  Roberto Pirrone,et al.  Intelligent Agents supporting user interactions within self regulated learning processes , 2010 .

[8]  Gerhard Weiss,et al.  Multiagent Systems , 1999 .

[9]  L. Shapley,et al.  Stochastic Games* , 1953, Proceedings of the National Academy of Sciences.

[10]  Nikos Vlassis,et al.  A Concise Introduction to Multiagent Systems and Distributed AI , 2003 .

[11]  Andrew G. Barto,et al.  Reinforcement learning , 1998 .

[12]  Demetrio A. Ovalle,et al.  ALLEGRO: Teaching/Learning Multi-Agent Environment using Instructional Planning and Cases- Based Reasoning (CBR) , 2007 .

[13]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[14]  Janette Cardoso,et al.  MathTutor: A Multi-Agent Intelligent Tutoring System , 2004, AIAI.

[15]  Sylvie Pesty,et al.  The Baghera project: a multi-agent architecture for human learning , 2003 .

[16]  B. De Schutter,et al.  Reinforcement learning for multi-agent systems , 2006 .

[17]  Pier Giuseppe Rossi.  Learning Environment With Elements Of Artificial Intelligence , 2009 .

[18]  Kenneth D. Forbus,et al.  RoboTA: An Agent Colony Architecture for Supporting Education , 1998 .

[19]  Ville Könönen.  Multiagent reinforcement learning in Markov games : asymmetric and symmetric approaches , 2004 .

[20]  Kenneth D. Forbus,et al.  RoboTA (poster): an agent colony architecture for supporting education , 1998, AGENTS '98.

[21]  Patrícia Augustin Jaques,et al.  Considering Student's Emotions in Computational Educational Systems , 2003 .

[22]  Ricardo Azambuja Silveira,et al.  FIPA COMPLIANT PEDAGOGICAL AGENTS IN DISTRIBUTED INTELLIGENT LEARNING ENVIRONMENTS , .

[23]  Abdelkader Adla,et al.  A Multi-agent Framework for Group Decision Support System: Application to a Boiler Combustion Management System (GLZ) , 2009 .

[24]  Demetrio Arturo Ovalle Carranza,et al.  ALLEGRO: Teaching/Learning Multi-Agent Environment using Instructional Planning and Cases- Based Reasoning (CBR) , 2007, CLEI Electron. J..

[25]  Joung-Souk Sung,et al.  Application Protocol Design for Collaborative Learning , 2009 .

[26]  Martin Appl,et al.  Model based reinforcement learning in continuous environments , 2001 .

[27]  Sean R Eddy,et al.  What is dynamic programming? , 2004, Nature Biotechnology.