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Teleologies: Objects, Actions and Functions

  • Fausto Giunchiglia
  • Mattia Fumagalli
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10650)

Abstract

We start from the observation that the notion of concept, as it is used in perception, is distinct and different from the notion of concept, as it is used in knowledge representation. In earlier work we called the first notion, substance concept and the second, classification concept. In this paper we integrate these two notions into a general theory of concepts that organizes them into a hierarchy of increasing abstraction from what is perceived. Thus, at the first level, we have objects (which roughly correspond to substance concepts), which represent what is perceived (e.g., a car); at the second level we have actions, which represent how objects change in time (e.g., move); while, at the third level, we have functions (which roughly correspond to classification concepts), which represent the expected behavior of objects as it is manifested in terms of “an object performing a certain set of actions” (e.g., a vehicle). The main outcome is the notion of Teleology, where teleologies provide the basis for a solution to the problem of the integration of perception and reasoning and, more in general, to the problem of managing the diversity of knowledge.

Keywords

Conceptual modeling Perception Knowledge 

References

  1. 1.
    Giunchiglia, F., Mattia F.: Concepts as (recognition) abilities. In: Proceedings of the 9th International Conference Formal Ontology in Information Systems (FOIS 2016), vol. 283, p. 153. IOS Press (2016)Google Scholar
  2. 2.
    Millikan, R.G.: Biosemantics. J. Philos. 86(6), 281–297 (1989)CrossRefGoogle Scholar
  3. 3.
    Prinz, J.: Beyond appearances: the content of sensation and perception. In: Perceptual Experience, pp. 434–460 (2006)CrossRefGoogle Scholar
  4. 4.
    Millikan, R.G.: On Clear and Confused Ideas: An Essay About Substance Concepts. Cambridge University Press, Cambridge (2000)CrossRefGoogle Scholar
  5. 5.
    Forsyth, D.A., Jean, P.: Computer Vision: A Modern Approach. Prentice Hall, Upper Saddle River (2011)Google Scholar
  6. 6.
    Sowa, J.F.: Knowledge Representation: Logical, Philosophical, and Computational Foundations, vol. 13. Brooks/Cole, Pacific Grove (2000)Google Scholar
  7. 7.
    Gruber, T.R.: Toward principles for the design of ontologies used for knowledge sharing. Int. J. Hum.-Comput. Stud. 43(5–6), 907–928 (1995)CrossRefGoogle Scholar
  8. 8.
    Giunchiglia, F.: Managing diversity in knowledge. In: Keynote Talk, European Conference on Artificial Intelligence (ECAI-2006) (2006). http://www.disi.unitn.it/~fausto/knowdive.pptGoogle Scholar
  9. 9.
    Bird, G.: Kant’s Theory of Knowledge: An Outline of One Central Argument in the ‘Critique of Pure Reason, vol. 1. Routledge, Abingdon (2016)CrossRefGoogle Scholar
  10. 10.
    Giunchiglia, F., Khuyagbaatar B., Gabor, B.: Understanding and exploiting language diversity. In: IJCAI (2017)Google Scholar
  11. 11.
    Gibson, J.J.: The theory of affordances. In: Perceiving, Acting, and Knowing: Toward an Ecological Psychology, pp. 67–82 (1977)Google Scholar
  12. 12.
    Şahin, E., Çakmak, M., Doğar, M.R., Uğur, E., Üçoluk, G.: To afford or not to afford: a new formalization of affordances toward affordance-based robot control. Adapt. Behav. 15(4), 447–472 (2007)CrossRefGoogle Scholar
  13. 13.
    Ortmann, J., Kuhn, W.: Affordances as qualities. In: FOIS, pp. 117–130 (2010)Google Scholar
  14. 14.
    Millikan, R.G.: Language, Thought, and Other Biological Categories: New Foundations for Realism. MIT press, Cambridge (1984)Google Scholar
  15. 15.
    Rosch, E., Mervis, C.B., Gray, W.D., Johnson, D.M., Boyes-Braem, P.: Basic objects in natural categories. Cogn. Psychol. 8(3), 382–439 (1976)CrossRefGoogle Scholar
  16. 16.
    Giunchiglia, F., Walsh, T.: A theory of abstraction. Artif. Intell. 57(2–3), 323–389 (1992)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Chen, L., Nugent, C.D., Wang, H.: A knowledge-driven approach to activity recognition in smart homes. IEEE Trans. Knowl. Data Eng. 24(6), 961–974 (2012)CrossRefGoogle Scholar
  18. 18.
    Rodríguez, N.D., Cuéllar, M.P., Lilius, J., Calvo-Flores, M.D.: A survey on ontologies for human behavior recognition. ACM Comput. Surv. (CSUR) 46(4), 43 (2014)CrossRefGoogle Scholar
  19. 19.
    Ni, Q., Pau de la Cruz, I., García Hernando, A.B.: A foundational ontology-based model for human activity representation in smart homes. J. Ambient Intell. Smart Environ. 8(1), 47–61 (2016)CrossRefGoogle Scholar
  20. 20.
    Barker, P., Campbell, L.M.: What is schema.org? LRMI (2015). Accessed 21 Apr 2014Google Scholar
  21. 21.
    Masolo, C., Borgo, S., Gangemi, A., Guarino, N., Oltramari, A., Oltramari, R., Schneider, L.: Lead Partner ISTC-CNR, Ian Horrocks. WonderWeb Deliverable D17. The WonderWeb Library of Foundational Ontologies and the DOLCE ontology (2002)Google Scholar
  22. 22.
    Niles, I., Pease, A.: Towards a standard upper ontology. In: Proceedings of the International Conference on Formal Ontology in Information Systems, vol. 2001, pp. 2–9. ACM (2001)Google Scholar
  23. 23.
    Millikan, R.G.: Varieties of Meaning: The 2002 Jean Nicod Lectures. MIT Press, Cambridge (2004)Google Scholar
  24. 24.
    Searle, J.R.: Social ontology and political power. In: Friederick, S.F. (ed.) Socializing Metaphysics: The Nature of Social Reality, pp. 195–210 (2003)Google Scholar
  25. 25.
    Macdonald, G., Papineau, D. (eds.): Teleosemantics. Clarendon Press, Wotton-under-Edge (2006)Google Scholar
  26. 26.
    Sober, E., Papineau, D.: Causal factors, causal inference, causal explanation. Proc. Aristot. Soc. Suppl. Vol. 60, 97–136 (1986)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Information Engineering and Computer Science (DISI)University of TrentoPovo, TrentoItaly

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