PEGASE: A Generic and Adaptable Intelligent System for Virtual Reality Learning Environments

The context of this research is the creation of human learning environments using virtual reality. We propose the integration of a generic and adaptable intelligent tutoring system (Pegase) into a virtual environment. The aim of this environment is to instruct the learner, and to assist the instructor. The proposed system is created using a multi-agent system. This system emits a set of knowledge (actions carried out by the learner, knowledge about the field, etc.) which Pegase uses to make informed decisions. Our study focuses on the representation of knowledge about the environment, and on the adaptable pedagogical agent providing instructive assistance.

[1]  Ronan Querrec,et al.  Multiagents systems for virtual environment for training. application to fire-fighting. , 2004 .

[2]  Nicolas Marion,et al.  GASPAR : Aviation Management on an Aircraft Carrier Using Virtual Reality , 2007, 2007 International Conference on Cyberworlds (CW'07).

[3]  Naïma El-Kechaï,et al.  A Plan Recognition Process, Based on a Task Model, for Detecting Learner's Erroneous Actions , 2006, Intelligent Tutoring Systems.

[4]  Roy M. TurnerDepartment.  Context-sensitive Reasoning for Autonomous Agents and Cooperative Distributed Problem Solving , 1993 .

[5]  Domitile Lourdeaux,et al.  HERA: Learner Tracking in a Virtual Environment , 2008, Int. J. Virtual Real..

[6]  Jean François Richard,et al.  Les activités mentales : comprende, raisonner, trouver des solutions , 1990 .

[7]  John H. Holland,et al.  Escaping brittleness: the possibilities of general-purpose learning algorithms applied to parallel rule-based systems , 1995 .

[8]  Stewart W. Wilson.  Hierarchical Credit Allocation in a Classifier System , 1987, IJCAI.

[9]  D. Herviou,et al.  AReViRoad: A Virtual Reality Tool for Traffic Simulation , 2006 .

[10]  Domitile Lourdeaux,et al.  Modelling Autonomous Virtual Agent Behaviours in a Virtual Environment for Risk , 2008, Int. J. Virtual Real..

[11]  Stewart W. Wilson,et al.  Noname manuscript No. (will be inserted by the editor) Learning Classifier Systems: A Survey , 2022 .

[12]  Daniel Thalmann,et al.  Modeling Objects for Interaction Tasks , 1998, Computer Animation and Simulation.

[13]  Patrick Brézillon,et al.  About Some Relationships between Knowledge and Context , 2001, CONTEXT.

[14]  W. Lewis Johnson,et al.  Animated Agents for Procedural Training in Virtual Reality: Perception, Cognition, and Motor Control , 1999, Appl. Artif. Intell..

[15]  Robert Hubal,et al.  Embodied Tutors for Interaction Skills Simulation Training , 2008, Int. J. Virtual Real..

[16]  Richard A. Blade.  A Short History of The International Journal of Virtual Reality , 2006, Int. J. Virtual Real..

[17]  Ronan Querrec,et al.  Modeling of Errors Realized by a Human Learner in Virtual Environment for Training , 2009, Int. J. Comput. Commun. Control.