Abstract
SIAM is a knowledge-based system designed for diagnosis aid, during practical work sessions, for students in higher education (polytechnic or university level). The system works on various fields of application but this paper will focus on electronics. SIAM helps students on the spot and teaches them a diagnosis method when they are confronted with technical problems during practical work. A friendly interface makes it easy to use.
Most of the knowledge useful for the system is represented through models and descriptives. The advantages of this approach are both technical and pedagogical, allowing easier acquisition of knowledge, reusability of the models, and construction of relevant explanations about the diagnosis methodology. Maintenance aspects have also been considered so that the evolution of the system can be directly monitored by the teachers.
A series of experiments using SIAM has enabled us to assert that nowadays knowledge-based systems like SIAM can be easily developed and are suitable tools for students and teachers.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Anderson, J.R.: The Expert Module. Foundations of Intelligent Tutoring Systems, p. 21–53. LEA. Hillsdale. New Jersey. 1988.
Brown, J.S., Burton, R.R.: Multiple representations of Knowledge for tutorial reasoning (SOPHIE). Representation and understanding. Academic Press. New York. 1975.
Brown, J.S., Burton, R.R., De Kleer, J.: Pedagogical, natural language and knowledge engineering techniques in SOPHIE I, II, III. Xerox Palo Alto Research Center. 1981.
Burton, R.R.: The Environment Module of Intelligent Tutoring Systems. Foundations of Intelligent Tutoring Systems, p. 109–142. LEA. Hillsdale. New Jersey. 1988.
Clancey, W.J.: Methodology for Building an Intelligent Tutoring System. Method and Tactics in Cognitive Science. Kintsch, Miller & Poison Editors. Hillsdale. 1984.
Clancey, W.J.: From Guidon to Neomycin and Heracles in Twenty Short Lessons. ONR Final Report 1979-1985. Stanford Knowledge Systems Laboratory. 1986.
Courtois, J.: Système intelligent pour l’apprentissage du diagnostic technique. Congrès ITS 88. Montréal. 1988.
Courtois, J.: Les apports de l’informatique dans la formation du raisonnement scientifique appliqué. Colloque “Les finalités de l’enseignement scientifique en question”. Marseille. 1989.
Courtois, J.: SIAM: un système de diagnostic qui s’adapte aisément à de nouveaux domaines et qui enseigne sa méthode. Thèse de Doctorat de l’Université Paris VI. 1990.
Courtois, J.: Practical Work Aid: Knowledge Representation in a Model Based AI System. Intelligent Learning Environments and Knowledge Acquisition in Physics. NATO ASI Series F Vol. 86. 1992.
Dague, P., Devès, P., Raiman, O.: Troubleshooting: when modeling is the trouble. AAAI 1987. Seattle. 1987.
De Kleer, J., Williams, B.C.: Diagnosis with Behavioral Modes. 11th IJCAI 89, p. 1324-1330. 1989.
Laurière, J.L.: Intelligence Artificielle (tome2) Représentation des connaissances. Eyrolles. Paris. 1988.
Marrakchi, M.: Représentation des connaissances pour l’aide au diagnostic industriel: application au système expert S.E.DIAG. Thèse de Docteur Ingénieur. Université de Valenciennes. 1986.
Moustafiadès, J.: Formation au diagnostic technique: l’apport de l’intelligence artificielle. Masson. Paris. 1990.
Nicaud, J.F., Vivet, M.: Les tuteurs intelligents: réalisations et tendances de recherches. TSI vol.7, no.1. 1988.
Paliès, O.: Métaconnaissance pour la modélisation de l’élève. Contribution au diagnostic cognitif par système expert. Thèse de Doctorat d’Université. Université Paris VI. 1988.
Pitrat, J.: Métaconnaissance, Futur de l’Intelligence Artificielle. Hermès. Paris. 1990.
Reiter, R.: A Theory of Diagnosis from First Principles. Artificial Intelligence 32, p. 57–95. 1987.
Raiman, O.: Order of magnitude reasoning. AAAI 1986, p. 100-104. Philadelphia. 1986.
Raiman, O.: Diagnosis as a trial: The alibi principle. Model Based Diagnosis International Workshop, July 25-27. Paris. 1989.
Vivet, M., Futtersack, M., Labat, J.M.: Métaconnaissances dans les tuteurs intelligents. Congrès ITS 88. Montréal. 1988.
White, B.Y., Frederiksen, J.R.: Qualitative models and intelligent learning environments. Artificial Intelligence and Education. Ablex Publishing Corporation. Norwood. 1987.
Woods, W.A.: Transition network grammars for natural language analysis. Communications of the ACM vol.13. 1970.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1993 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Courtois, J. (1993). SIAM: A Knowledge-Based System for Practical Work. In: Caillot, M. (eds) Learning Electricity and Electronics with Advanced Educational Technology. NATO ASI Series, vol 115. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-02878-0_18
Download citation
DOI: https://doi.org/10.1007/978-3-662-02878-0_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-08157-6
Online ISBN: 978-3-662-02878-0
eBook Packages: Springer Book Archive