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International Symposium on Methodologies for Intelligent Systems

ISMIS 1993: Methodologies for Intelligent Systems pp 548–557Cite as

Multi-Context systems as a tool to model temporal evolution

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 689))

Abstract

Contexts are defined as axiomatic formal systems. More than one context can be defined, each one modeling/solving (part of) the problem. The (global) model/solution of the problem is obtained making contexts communicate via bridge rules. Bridge rules and contexts are the components of Multi Context systems. In this paper we want to study the applicability of multi contexts systems to reason about temporal evolution. The basic idea is to associate a context to each temporal interval in which the “model” of the problem does not change (corresponding to a state of the system). Switch among contexts (corresponding to modifications in the model) are controlled via a meta-theoric context responsible to keep_track_of the temporal evolution. In this way (i) we keep a clear distinction between the theory describing the particular system at hand and the theory necessary for predicting the temporal evolution (ii) we have simple object level models of the system states and (iii) the theorem prover can faster analize and answer to queries about a particular state. The temporal evolution of a U-tube is taken as an example to show both the proposed framework and the GETFOL implementation.

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References

  1. D.G. Bobrow. Qualitative reasoning about physical systems. Artificial Intelligence., 24, 1984.

    Google Scholar 

  2. J.H. De Kleer and J.S. Brown. A qualitative physics based on confluences. Artificial Intelligence, 24:7–83, 1984.

    Google Scholar 

  3. K.D. Forbus. Qualitative process theory. Artificial Intelligence., 24:85–168, 1984.

    Google Scholar 

  4. F. Giunchiglia. Multilanguage systems. In Proceedings of AAAI Spring Symposium on Logical Formalizations of Commonsense Reasoning, 1991. Also IRST-Technical Report no. 9011–17.

    Google Scholar 

  5. F. Giunchiglia and A. Smaill. Reflection in constructive and nonconstructive automated reasoning. In H. Abramson and M. H. Rogers, editors, Proc. Workshop on Meta-Programming in Logic Programming, pages 123–145. MIT Press, 1989. Also IRST-Technical Report 8902-04 and DAI Research Paper 375, University of Edinburgh.

    Google Scholar 

  6. F. Giunchiglia and P. Traverso. GETFOL User Manual-GETFOL version 1. Manual 9109-09, IRST, Trento, Italy, 1991. Also MRG-DIST Technical Report 9107-01, DIST, University of Genova.

    Google Scholar 

  7. F. Giunchiglia and R.W. Weyhrauch. A multi-context monotonic axiomatization of inessential non-monotouicity. In D. Nardi and P. Maes, editors, Meta-level architectures and Reflection, pages 271–285. North Holland, 1988. Also MRG-DIST Technical Report 9105-02, DIST, University of Genova, Italy.

    Google Scholar 

  8. B.J. Kuipers and C. Chiu. Taming intractable branching in qualitative simulation. In Proc. IJC'AI conference, pages 1079-1085. International Joint Conference on Artificial Intelligence, 1987.

    Google Scholar 

  9. S.O. Kleene. Introduction to Metamathematics. North Holland, 1952.

    Google Scholar 

  10. B.J. Kuipers. Qualitative simulation. Artificial Intelligence, 29:289–338, 1986.

    Google Scholar 

  11. J. McCarthy. Generality in Artificial Intelligence. In J. McCarthy, editor, Formalizing Common Sense—Papers by John McCarthy, pages 226–236. Ablex Publishing Corporation, 1990.

    Google Scholar 

  12. J. McCarthy. Notes on formalizing context. Unpublished, 1991.

    Google Scholar 

  13. D. Prawitz. Natural Deduction—A proof theoretical study. Almquist and Wiksell, Stockholm, 1965.

    Google Scholar 

  14. Y. Shoham. Varietes of context. In V. Lifschitz, editor, Artificial Intelligence and Mathematical Theory of Computation—Papers in honor of John McCarthy, pages 393–408. Academic Press, 1991.

    Google Scholar 

  15. D.S. Weld and J.H. De Kleer. Readings in Qualitative Reasoning about Physical Systems. Morgan Kaufmann Publishers, Inc., 95, First Street, Los Altos, CA 94022, 1989.

    Google Scholar 

  16. R.W. Weyhrauch. Prolegomena to a Theory of Mechanized Formal Reasoning. Arttf. Intell., 13(1): 133–176, 1980.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Mechanized Reasoning Group, DIST-University of Genoa, Via Opera Pia 11A, 16145, Genoa, Italy

    Mauro Di Manzo & Enrico Giunchiglia

Authors
  1. Mauro Di Manzo
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  2. Enrico Giunchiglia
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Editor information

Jan Komorowski Zbigniew W. Raś

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© 1993 Springer-Verlag Berlin Heidelberg

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Di Manzo, M., Giunchiglia, E. (1993). Multi-Context systems as a tool to model temporal evolution. In: Komorowski, J., Raś, Z.W. (eds) Methodologies for Intelligent Systems. ISMIS 1993. Lecture Notes in Computer Science, vol 689. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-56804-2_51

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  • DOI: https://doi.org/10.1007/3-540-56804-2_51

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-56804-9

  • Online ISBN: 978-3-540-47750-1

  • eBook Packages: Springer Book Archive

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