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Abstract

Drawn from the lessons learned in an application for the subway company in Paris, we pointed out that operators used practices instead of the procedures developed by the company, practices appearing like contextualization of the procedures taking into account specificity of the task at hand and the current situation. This leads us to propose, first, a working definition of context at a theoretical level, and, second, its implementation in a software called Contextual Graphs. In this paper, we present the results of the complete loop, showing how the theoretical view is intertwined with the implemented one. Several results of the literature are discussed too. Beyond this internal coherence of our view on context, we consider knowledge acquisition, learning and explanation generation in our framework. Indeed, these tasks must be considered as integrated naturally with the task at hand of the user.

Keywords

External Knowledge Current Focus Contextual Element Contextual Knowledge Recombination Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Brézillon, P.: Representation of procedures and practices in contextual graphs. The Knowledge Engineering Review 18(2), 147–174 (2003)CrossRefGoogle Scholar
  2. 2.
    Brezillon, P.: Task-realization models in Contextual Graphs. In: Dey, A.K., Kokinov, B., Leake, D.B., Turner, R. (eds.) CONTEXT 2005. LNCS, vol. 3554, pp. 55–68. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  3. 3.
    Brézillon, P., Pomerol, J.-C.: Contextual knowledge sharing and cooperation in intelligent assistant systems. Le Travail Humain 62(3), 223–246 (1999)Google Scholar
  4. 4.
    Brézillon, P., Cavalcanti, M., Naveiro, R., Pomerol, J.-C.: SART: An intelligent assistant for subway control. Pesquisa Operacional, Brazilian Operations Research Society 20(2), 247–268 (2000)MATHGoogle Scholar
  5. 5.
    McCarthy, J.: Notes on formalizing context. In: Proceedings of the 13th IJCAI, vol. 1, pp. 555–560 (1993)Google Scholar
  6. 6.
    Pasquier, L., Brézillon, P., Pomerol, J.-C.: Learning and explanation in a context-based representation: Application to incident solving on subway lines. In: Jain, R., Abraham, A., Faucher, C., van der Zwaag, J. (eds.) Innovations in Knowledge Engineering. International Series on Advanced Intelligence, ch. 6, pp. 129–149 (2003)Google Scholar
  7. 7.
    Schank, R.C.: Dynamic memory, a theory of learning in computers and people. Cambridge University Press, Cambridge (1982)Google Scholar
  8. 8.
    Serafini, L., Giunchiglia, F., Mylopoulos, J., Bernstein, P.: Local relational model: A logical formalization of database coordination. In: Blackburn, P., Ghidini, C., Turner, R.M., Giunchiglia, F. (eds.) CONTEXT 2003. LNCS (LNAI), vol. 2680, pp. 286–299. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  9. 9.
    Sowa, J.F.: Knowledge Representation: Logical, Philosophical, and Computational Foundations. Brooks Cole Publishing Co., Pacific Grove (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Patrick Brézillon
    • 1
  1. 1.LIP6, case 169ParisFrance

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