A Web-based Intelligent Virtual Learning Environment for Industrial Continuous Improvement

Training in an industrial context is becoming more dynamic and time consuming due to the daily emergence of new technologies and the onset of globalization. People from higher education institutions and industries have applied various heuristic teaching methods in order to inspire students to acquire knowledge in a manufacturing and industrial context. Artificial intelligence techniques has been used for many years in educational systems, however the presentation of domain knowledge is one of the most concerning issues which stems the evolvement of the industrial training systems development. In this paper we present a Web-based intelligent virtual learning environment which is designed for the training of industrial continuous improvement techniques. Emphasis is placed on neural network and multi-agent techniques to build state-of-art domain knowledge models which demonstrate realistic industrial environments.

[1]  Chilukuri Krishna Mohan,et al.  Frontiers of expert systems - reasoning with limited knowledge , 2000, The Kluwer international series in engineering and computer science.

[2]  Claude Frasson,et al.  Designing a multi-strategic intelligent tutoring system for training in industry , 1998 .

[3]  David Benyon,et al.  Adaptive Systems : from intelligent tutoring to autonomous agents 1 , 1993 .

[4]  Diane J. Litman,et al.  ITSPOKE: An Intelligent Tutoring Spoken Dialogue System , 2004, NAACL.

[5]  John Seely Brown,et al.  Sophisticated Instructional Environment for Teaching Electronic Troubleshooting. , 1974 .

[6]  Etienne Wenger,et al.  Artificial Intelligence and Tutoring Systems: Computational and Cognitive Approaches to the Communication of Knowledge , 1987 .

[7]  Peter Brusilovsky,et al.  ELM-ART: An Intelligent Tutoring System on World Wide Web , 1996, Intelligent Tutoring Systems.

[8]  Mia Stern,et al.  Applications of AI in education , 1996, CROS.

[9]  VII Acknowledgment , 1981 .

[10]  Tom Routen,et al.  Intelligent Tutoring Systems , 1996, Lecture Notes in Computer Science.

[11]  Tom Murray,et al.  Authoring Intelligent Tutoring Systems: An analysis of the state of the art , 1999 .

[12]  Edson Pacheco Paladini,et al.  Artificial intelligence approach to support statistical quality control teaching , 2006, Comput. Educ..

[13]  Matthew P. J. Pepper,et al.  A Web-based virtual factory and simulator for industrial statistics , 2004, Proceedings of the 2004 Winter Simulation Conference, 2004..

[14]  Christopher M. Bishop,et al.  Novelty detection and neural network validation , 1994 .

[15]  William J. Clancey,et al.  Tutoring rules for guiding a case method dialogue , 1979 .