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Contextual Categorization: A Mechanism Linking Perception and Knowledge in Modeling and Simulating Perceived Events as Actions

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Modeling and Using Context (CONTEXT 2001)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2116))

Abstract

The specific objective of this paper is to introduce the computer model ACACIA (Action by Contextually Automated Categorizing Interactive Agents) capable of simulating the way in which context is taken into account for the interpretation of perceived actions elaborated by a number of autonomous moving agents in a bidimensional space. With this in mind, we will examine some different modeling approaches in Artificial Intelligence and Artificial Life and emphasize the strong and weak points of each approach in relation to the set of issues addressed by our theory based on Contextual Categorization. Second, we provide a theoretical explanation of how contextual categorization accounts for temporal and environmental context to interpret ongoing situations in terms of perceived action. Finally, we describe the computer implementation of ACACIA, and we propose a preliminary simulation of a simple situation using StarLogo software.

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References

  1. Ayes, D., & Shah, M. (1998): Monitoring Human Behavior in an Office Environment. In Proceeding of the IEEE Computer Society Workshop: The Interpretation ofVisual Motion, CVPR’98, Santa Barbara, CA, June 22. 65–72.

    Google Scholar 

  2. Barbut, M., & Monjardet, B. (1970). Ordre et Classification: algébre et combinatoire. Paris. Hachette.

    Google Scholar 

  3. Barsalou, L.W. (1983). Ad Hoc Categories. Memory and Cognition, 11, 211–227.

    Google Scholar 

  4. Barsalou, L.W. (1991). Deriving categories to achieve goals. In G.H. Bower (Ed.), The psychology of learning and motivation: Advances in Research and Theory, Vol. 27, (1–64). New York: Academic Press.

    Google Scholar 

  5. Brooks, R.A. (1991). Intelligence without representation. Artificial Intelligence, 47, 139–159.

    Article  Google Scholar 

  6. Cordier, F., & Tijus, C. A. (2000). Propriétés d’Objets: Typologie et Organisation. Actes du XXVI/ Congrés International de Psychologie, Montréal, 26–29 juillet 2000.

    Google Scholar 

  7. Gibson, J. (1977). The Theory of affordance. In R.E. Shaw, & J. Brandsford (Eds.) Perceiving, acting, and knowing. Hillsdale, N.J.: Erlbaum

    Google Scholar 

  8. Gibson, J. (1979). The Ecological Approach to Visual Perception. Boston, M.A.: Houghton Mifflin.

    Google Scholar 

  9. Intille, S. S. (1999). Visual recognition of Multy-Agent action. Ph.D. Thesis, Massachusetts Institute of Technology.

    Google Scholar 

  10. Intille, S. S., & Bobick, A.F. (1998). Representation and Visual Recognition of Complex, Multi-agent Actions using Beliefs Networks. In Proceeding of the IEEE Computer Society Workshop: The Interpretation ofVisual Motion, CVPR’98, Santa Barbara, CA, June 22. 73–80.

    Google Scholar 

  11. Langton, C.G.(1989). Artificial life. In C.G. Langton (Ed.), Artificial life. The proceedings of an interdisciplinary workshop on the synthesis and simulation of living systems (pp. 1–47). Redwood City, Ca: Addison-Wesley.

    Google Scholar 

  12. Maes, P.(1998). Modeling adaptive autonomous agents. In C.G. Langton (Ed.), Artificial life. An overview (pp. 135–162). MIT Press.

    Google Scholar 

  13. Miller, G. A., & Johnson-Laird, P. N. (1976). Language and Perception. Cambridge, MA: Harvard Univ. Press.

    Google Scholar 

  14. Oatley, K. & Yuill, N. (1985) Perception of personal and interpersonal action in a carton film. British Journal of Social Psychology 24, 115, 124.

    Google Scholar 

  15. Poitrenaud, S. (1995). The Procope Semantic Network: an alternative to action grammars. International Journal of Human-Computer Studies, 42, 31–69.

    Article  Google Scholar 

  16. Quera, V., Solanas, A., Salafranca, L., Beltran, F.S., & Herrando, S. (2000). A dynamic model for inter-agent distances. In Meyer, J.-A., Berthoz, A., Floreano, D., Roitblat, H.L., Wilson, S. (Eds.), From Animals to Animats 6, SAB2000 Proceedings Supplement. Honolulu (Hawaii): USA.

    Google Scholar 

  17. Quera, V., Solanas, A., Salafranca, Ll., Beltran, F.S., & Herrando, S. (2000). P-SPACE: A program for simulating spatial behavior in small groups. Behavior Research Methods, Instruments, and Computers, 32(1), 191–196.

    Google Scholar 

  18. Resnick, M. (1994). Turtles, Termites, and Traffic Jams: Explorations in Massively Parallel Microworlds. Cambridge, MA: MIT Press.

    Google Scholar 

  19. Richard, J.F., & Tijus, C. A. (1998). Modeling the Affordances of Objects in Problem Solving. In A.C. Quelhas & F. Pereira (Ed.), Cognition and Context. Lisboa ISPA, 293–315.

    Google Scholar 

  20. Ross, B.H. (1996). Category learning as problem solving. In D.L. Medin (Ed.), The Psychology of Learning and Motivation: Advances in Research and Theory, Vol. 35 (165–192). San Diego, CA: Academic Press.

    Google Scholar 

  21. Schyns, P., Goldstone, R.L., & Thilbaut, J.-P. (1998). The development of features in object concepts. Behavioral and Brain Sciences 21(1): 1–54.

    Article  Google Scholar 

  22. Thibadeau, R. (1986). Artificial Perception of Actions, Cognitive Science, 10, 177–149.

    Article  Google Scholar 

  23. Tijus, C. A., & Moulin, F. (1997). L’assignation de signification étudiée á partir de textes d’ histoires drôles. L’Année Psychologique, 97, 33–75.

    Article  Google Scholar 

  24. Tijus, C. A., & Poitrenaud, S. (1997). Modeliser l’Affordance des Objets. Actes du 6éme colloque: Sciences Cognitives, Individus et Société, p 57–65.

    Google Scholar 

  25. Tijus, C.A., (1996). Assignation de signification et construction de la réprésentation. Habilitation á diriger les recherches. Université de Paris 8.

    Google Scholar 

  26. Turner, R. M. (1999). Model of Explicit Context Representation and Une for Intelligent Agents. In J.G. Carbonell & J. Siekmann (eds), Lectures Notes in Artificial Intelligence, vol. 1688, Modeling and Using Context, (pp. 375–388). New-York: Springer.

    Google Scholar 

  27. Yamauchi, T., & Markman, A. (1998). Category Learning by Inference and Classification. Journal of Memory and Language, 39, 124–148.

    Article  Google Scholar 

  28. Zibetti, E., Hamilton, E. & Tijus, C.A. (under revision). Contextual Categorization in Interpreting Perceived Actions: The Role of Objects Properties. Cognitive Science.

    Google Scholar 

  29. Zibetti, E., Hamilton, E., & Tijus C.A. (1999). The role of Context in Interpreting Perceived Events as Action. In J.G. Carbonell & J. Siekmann (eds), Lectures Notes in Artificial Intelligence, vol. 1688, Modeling and Using Context, (pp. 431–441). New-York: Springer.

    Google Scholar 

  30. Zibetti, E., Poitrenaud, S., & Tijus, C.A. (in press). La construction de la représentation de l’action perçue. Intellectica.

    Google Scholar 

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© 2001 Springer-Verlag Berlin Heidelberg

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Zibetti, E., Quera, V., Beltran, F.S., Tijus, C. (2001). Contextual Categorization: A Mechanism Linking Perception and Knowledge in Modeling and Simulating Perceived Events as Actions. In: Akman, V., Bouquet, P., Thomason, R., Young, R. (eds) Modeling and Using Context. CONTEXT 2001. Lecture Notes in Computer Science(), vol 2116. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44607-9_30

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  • DOI: https://doi.org/10.1007/3-540-44607-9_30

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42379-9

  • Online ISBN: 978-3-540-44607-1

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