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Representing Operational Knowledge by Contextual Graphs

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Advances in Artificial Intelligence (IBERAMIA 2000, SBIA 2000)

Abstract

In various industrial fields, the operators use pre-designed procedures either to solve problems or for troubleshooting. In the Parisian subway, such procedures exist since 1900. However, these procedures are not always exactly suited to the case at hand, and the operators generally prefer to customize a solution than to rely on fixed procedures. A new generation of decision support systems, so-called “intelligent” assistant systems, offers more flexible possibilities of cooperation between the users and the system. SART is such a system, for its design, we have modeled operators’ activity to model the cooperation between the operators and the system. As a result, we introduce the contextual graph paradigm, which appears as a possible computer representation of schemes that are used in psychology to describe human activities.

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References

  • Béguin, P. (1994). Travailler avec la CAO en ingénierie industrielle: de l’individuel au collectifdans les activités avec instruments. Thèse de doctorat en ergonomie. Paris, CNAM.

    Google Scholar 

  • Brézillon, P. and Pomerol, J.-Ch. (1998). Using contextual information in decision making.Context Sensitive Decision Support Systems, Berkeley, D. et al. (Eds.), Chapman et al., London., pp 158–173.

    Google Scholar 

  • Brézillon, P., Gentile, C., Saker, I. and Secron., M. (1997) SART: A system for supporting operators with contextual knowledge. First International and Interdisciplinary Conference On Modelling and Using Context (CONTEXT’97), pages 209–222, Federal University of Rio de Janiero, 1997 (also available at http://www-poleia.lip6.fr/~brezil/Pages2/CONTEXT-97/index.html ).

  • Britanik J.M. & Marefat M.M. (1999) Hierarchically merging plans in decomposable domains. IEEE Trans. on Systems, Man, and Cybernetics, 29(1): 27–39.

    Article  Google Scholar 

  • Chandrasekaran, B., Johnson, T.R. and Smith, J.W. (1992). Task-structure analysis for knowledge modeling. Communications of the ACM, 35(9): 124–137.

    Article  Google Scholar 

  • Clancey (1979). Tutoring rules for guiding a case method dialogue. International Journal of Man-Machine Studies11(1), pp 25–49.

    Article  Google Scholar 

  • De Brito, G. & Boy, G. (1999). Situation awareness and procedure following. CSAPC’99, Villeneuve d’Ascq, Presses Universitaires de Valenciennes, pp 9–14.

    Google Scholar 

  • Duvenci-Lenga, S. (1997). Evolution de l’activité et des compétences en situation d’automatisation: le cas des machines-outils. Thèse de doctorat d’ergonomie. Paris: CNAM.

    Google Scholar 

  • Eco U. (1997) Kant et l’Ornithorynque. Paris: Grasset.

    Google Scholar 

  • Galinier, V. (1996). Apports de l’ergonomie à la conception d’instruments: concevoir autour des schèmes d’utilisation. Un exemple dans le domaine du transport routier. Thèse de doctorat en ergonomie. Paris: CNAM.

    Google Scholar 

  • Hayes-Roth B. & Hayes-Roth F. (1979). A cognitive model of planning, Cognitive Science, 3, 275–310.

    Google Scholar 

  • Humphreys, P. & Berkeley, D. (1992). Support for the synthesis and analysis of organisationalsystems in deciding on change. Decision Support Systems: Experiences and Expectations, T. Jelassi, M.R. Klein & W.M. Mayon-White (Eds.). Elsevier Science publishers, Amsterdam, North-Holland, pp. 29–50.

    Google Scholar 

  • Jensen, F. (1996). An introduction to Bayesian networks. UCL Press, London.

    Google Scholar 

  • Laville, V. & Zanarelli, C. (2000) La communication comme indicateur structurant de l’activité: illustration dans uns situation de régulation de métro. (submited)

    Google Scholar 

  • Leake D.B. (1996) Case-based reasoning: Experiences, lessons, and future directions. Chapter I: CBR in context: The present and future. Menlo Park: AAAI Press/MIT Press.

    Google Scholar 

  • Leplat J. (1985) Erreur humaine, fiabilité humaine dans le travail. In: A. Colin (Ed.), Collection Uiversitaire, pp. 100–120.

    Google Scholar 

  • Neapolitan, R. (1990). Probabilistic reasoning in expert systems. John Wiley & Sons, New York.

    Google Scholar 

  • Niang, D. (1999). Méthodes et techniques d’Intelligence Artificielle pour prévenir l’explosion combinatoire. Application au Gestionnaire d’Incidents de SART. Rapport de DEA IRO, LIP6, Paris.

    Google Scholar 

  • Oliver, R. & Smith, J. (1990). Influence Diagrams, Belief Nets and Decision analysis. Wiley Series in Probability and Mathematical Statistics, John Wiley & Sons, New York

    Google Scholar 

  • Pasquier, L. (2000). Raisonnements basés sur le contexte: Contextes procéduralisés, graphes contextuels et schémes d’action. Research Report LIP6 N.2000-010.

    Google Scholar 

  • Pasquier, L., Brézillon, P. & Pomerol, J.-Ch. (1999). Context and decision graphs in incident management on a subway line. Modeling and Using Context (CONTEXT-99). In: Lecture Notes in Artificial Intelligence, N° 1688, Springer Verlag, pp. 499–502.

    Google Scholar 

  • Pearl, J. (1988). Probabilistic reasoning in intelligent systems. Morgan Kaufmann publishers, San Mateo, California.

    Google Scholar 

  • Piaget, J. (1936). La naissance de l’intelligence chez l’enfant. Paris, Lausane.

    Google Scholar 

  • Pomerol, J.-Ch. & Brézillon, P. (1999). Dynamics between contextual knowledge and proceduralized context. Modeling and Using Context (CONTEXT-99). In: Lecture Notes in Artificial Intelligence, N° 1688, Springer Verlag, pp. 284–295.

    Google Scholar 

  • Rabardel, P. (1995) Les Hommes et les technologies. Approche cognitive des instruments contemporains. Paris: A. Colin.

    Google Scholar 

  • Raïffa, (1968). Decision Analysis, Mac Graw Hill.

    Google Scholar 

  • Schank, (1982). Dynamic memory, a theory of learning in computers and people. Cambridge University Press.

    Google Scholar 

  • Schank, and Alberson, (1975). Scripts, Plans, Goals and Understanding: an Inquiry into Human Knowledge Structures, L. Erlbaum, Hillsdale, NJ.

    Google Scholar 

  • Sowa, J.F. (1984). “Conceptual Structures: Information Processing in Mind and Machine”, Addison Wesley Publishing Company.

    Google Scholar 

  • Sowa, J.F. (1991). “Toward the expressive power of natural language”, In: Principles of Semantic Networks-Exploration in the representation of Knowledge, Morgan Kaufmann, San Mateo, CA, pp. 157–189.

    Google Scholar 

  • Vergnaud, G. (1985). Concepts et schémes dans la théorie opératoire de la représentation, Les Représentation, Psychologie Française 30 (3 et 4), pp 245–252.

    Google Scholar 

  • Watson I. & Perera S. (1998) A hierarchical case representation using context guided retrieval. Knowledge Based Systems Journal (forthcoming)

    Google Scholar 

  • Zanarelli C. (1998) Identifier des classes de situations caractéristiques: quelques critères de différenciation des situations sont utilisés par les chefs de régulation et les chefs de départs? Document interne RATP

    Google Scholar 

  • Zanarelli, C., Saker, I. & Pasquier, L. (1999) Un projet de coopération ergonomes / concepteurs autour de la conception d’un outil d’aide à la régulation du trafic du métro. Actes de la Conférence Ingénierie des Connaissances. Pages 171–180, Palaiseau.

    Google Scholar 

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Brezillon, P., Pasquier, L., Pomero, JC. (2000). Representing Operational Knowledge by Contextual Graphs. In: Monard, M.C., Sichman, J.S. (eds) Advances in Artificial Intelligence. IBERAMIA SBIA 2000 2000. Lecture Notes in Computer Science(), vol 1952. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44399-1_26

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  • DOI: https://doi.org/10.1007/3-540-44399-1_26

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41276-2

  • Online ISBN: 978-3-540-44399-5

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