Operationalization of the Blending and the Levels of Abstraction Theories with the Timed Observations Theory

Marc Le Goc, Fabien Vilar

Abstract

Providing a meaning to observations coming from humans (interviews) or machines (data sets) is a necessity to build adequate analysis and efficient models that can be used to take a decision in a given domain. Fauconnier and Turner demonstrates in 1998 the cognitive power of their Blending Theory where the blending of multiple conceptual networks is presented as a general-purpose, fundamental, indispensable cognitive operation to this aim. On the other hand, Floridi proposed in 2008 a theory of levels of abstraction as a fundamental epistemological method of conceptual analysis that can also be used to this aim. Both theories complete together but both lack of mathematical foundations to build an operational data and knowledge modeling method that helps and guides the Analysts and the Modeling Engineers. In this theoretical paper, we introduce the mathematical framework, based on the Timed Observations Theory, designed to build a method of abstraction merging together the Blending Theory and the Levels of Abstraction Theory. Up to our knowledge, this is the first mathematical theory allowing the operationalization of the Blending Theory and the Levels of Abstraction Theory. All over the paper, the mathematical framework is illustrated on an oral exchange between three persons observing a vehicle. We show that this framework allows to build a rational meaning of this exchange under the form of a superposition of three abstraction levels.

References

  1. Bredeweg, B. (1994). The CommonKads Library for Expertise Modelling, chapter Model-based diagnosis and prediction, p.121-153. IOPress.
  2. Fauconnier, G. and Turner, M. (1998). Conceptual integration networks. Cognitive Science, (22):133-187.
  3. Fauconnier, G. and Turner, M. (2003). Conceptual blending, form and meaning. Recherches en communication, n ? 19 (2003)., (19):57-86.
  4. Floridi, L. (2008). The method of levels of abstraction. Minds and Machines, 18:303-329.
  5. Floridi, L. (2010). Levels of abstraction and the turing test. Keybenetes, 39(3):423-440.
  6. Le Goc, M. (2004). Sachem. a real time intelligent diagnosis system based on the discrete event paradigm. Simulation, The Society for Modeling and Simulation International Ed., 80(11):591-617.
  7. Le Goc, M. (2006). Notion d'observation pour le diagnostic des processus dynamiques: Application à Sachem et à la découverte de connaissances temporelles. Hdr, Aix-Marseille University, Faculté des Sciences et Techniques de Saint Jéroˆme.
  8. Le Goc, M. and Ahdab, A. (2012). Learning Bayesian Networks From Timed Observations. LAP LAMBERT Academic Publishing GmbH & Co. KG.
  9. Le Goc, M., Barthelot, F., and Pascual, E. (2015). Emergence of regularities in the stochastic behavior of human. In IEEE International Conference on Data Mining Workshop, ICDMW 2015, Atlantic City, NJ, USA, November 14-17, 2015, pages 381-388. IEEE Computer Society.
  10. Le Goc, M. and Gaeta, M. (2004). Modeling Strutures in Generic Space, a Condition for Adaptiveness of Monitoring Cognitive Agent. Journal of Intelligent and Robotics Systems, 41(2-3):113-140.
  11. Mac Lane, S. (1978). Categories for the Working Mathematician, volume 5. Springer-Verlag New York.
  12. Newell, A. (1981). The knowledge level. AI Magazine, 2(2):1-20.
  13. Pomponio, L. and Le Goc, M. (2014). Reducing the gap between experts' knowledge and data: The tom4d methodology. Data & Knowledge Engineering, DOI 10.1016/j.datak.2014.07.006.
  14. Schreiber, G., Akkermans, H., Anjewierden, A., de Hoog, R., Shadbolt, N., de Velde, W. V., and Wielinga, B. (2000). Knowledge Engineering and Management: The CommonKADS Methodology. MIT Press.
  15. Zanni, C., Le Goc, M., and Frydman, C. (2006). Conceptual equivalence at knowledge level for diagnosis applications. KES, International Journal of Knowledge-Based and Intelligent Engineering Systems, 10(3):225-246.
Download


Paper Citation


in Harvard Style

Le Goc M. and Vilar F. (2017). Operationalization of the Blending and the Levels of Abstraction Theories with the Timed Observations Theory . In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-220-2, pages 364-373. DOI: 10.5220/0006111103640373


in Bibtex Style

@conference{icaart17,
author={Marc Le Goc and Fabien Vilar},
title={Operationalization of the Blending and the Levels of Abstraction Theories with the Timed Observations Theory},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2017},
pages={364-373},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006111103640373},
isbn={978-989-758-220-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Operationalization of the Blending and the Levels of Abstraction Theories with the Timed Observations Theory
SN - 978-989-758-220-2
AU - Le Goc M.
AU - Vilar F.
PY - 2017
SP - 364
EP - 373
DO - 10.5220/0006111103640373