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A Schema Based Model of the Praying Mantis

  • Giovanni Pezzulo
  • Gianguglielmo Calvi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4095)

Abstract

We present a schema-based agent architecture which is inspired by an ethological model of the praying mantis. It includes an inner state, perceptual and motor schemas, several routines, a fovea and a motor. We describe the design and implementation of the architecture and we use it for comparing two models: the former uses reactive, stimulus-response schemas; the latter involves also forward models, which are used by the schemas for generating predictions. Our results show an advantage in using anticipatory components inside the schemas.

Keywords

Forward Model Active Schema Motor Schema Predictive Success Command Neuron 
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.
    Arbib, M.A.: Schema Theory. In: Shapiro, S. (ed.) The Encyclopedia of Artificial Intelligence, 2nd edn., vol. 2, pp. 1427–1443. Wiley, Chichester (1992)Google Scholar
  2. 2.
    Arkin, R.C., Ali, K., Weitzenfeld, A., Cervantes-Pérez, F.: Behavioral models of the praying mantis as a basis for robotic behavior. Robotics and Autonomous Systems 32(1), 39–60 (2000)CrossRefGoogle Scholar
  3. 3.
    Barsalou, L.W.: Perceptual symbol systems. Behavioral and Brain Sciences 22, 577–600 (1999)Google Scholar
  4. 4.
    Bryson, J.: The Study of Sequential and Hierarchical Organisation of Behaviour via Artificial Mechanisms of Action Selection. M.Phil Thesis. University of Edinburgh (2000)Google Scholar
  5. 5.
    Crabbe, F.L.: Optimal and non-optimal compromise strategies in action selection. In: Proceedings of SAB 2004 (2004)Google Scholar
  6. 6.
    Demiris, Y., Khadhouri, B.: Hierarchical Attentive Multiple Models for Execution and Recognition (HAMMER). Robotics and Autonomous Systems Journal (2005)Google Scholar
  7. 7.
    Grush, R.: The emulation theory of representation: Motor control, imagery, and perception. Behavioral and Brain Sciences 27(3), 377–396 (2004)Google Scholar
  8. 8.
    Gurney, K.N., Prescott, T.J., Redgrave, P.: A computational model of action selection in the basal ganglia, I. A new functional anatomy. Biological Cybernetics 84, 401–410 (2001)MATHCrossRefGoogle Scholar
  9. 9.
    Hopfinger, J.B., Buonocore, M.H., Mangun, G.R.: The neural mechanisms of top-down attentional control. Nature Neuroscience 3(3), 284–291 (2000)CrossRefGoogle Scholar
  10. 10.
  11. 11.
  12. 12.
    Itti, L., Koch, C.: Computational modeling of visual attention. Nature Reviews Neuroscience 2(3), 194–203 (2001)CrossRefGoogle Scholar
  13. 13.
    Jacobs, R.A., Jordan, M.I., Nowlan, S.J., Hinton, G.E.: Adaptive mixtures of local experts. Neural Computation 3, 79–87 (1991)CrossRefGoogle Scholar
  14. 14.
    James, W.: The Principles of Psychology. Dover Publications, New York (1890)CrossRefGoogle Scholar
  15. 15.
    Kosko, B.: Neural Networks and Fuzzy Systems. Prentice Hall International Inc., Singapore (1992)MATHGoogle Scholar
  16. 16.
    Kosslyn, S.M., Sussman, A.L.: Roles of imagery in perception: Or, there is no such thing as immaculate perception. In: Gazzaniga, M. (ed.) The cognitive neurosciences, pp. 1035–1042. MIT Press, Cambridge (1994)Google Scholar
  17. 17.
    Kupfermann, I., Weiss, K.: The command neuron concept. Behavioral and Brain Sciences 1, 3–39 (1978)CrossRefGoogle Scholar
  18. 18.
    Maes, P.: A Bottom-Up Mechanism for Behavior Selection in an Artificial Creature. In: Meyer, J.A., Wilson, S.W. (eds.) Proceedings of SAB 1990, pp. 238–246. The MIT Press, Cambridge (1990)Google Scholar
  19. 19.
    O’Regan, J.K., Noe, A.: A sensorimotor account of vision and visual consciousness. Behavioral and Brain Sciences 24(5), 883–917 (2001)Google Scholar
  20. 20.
    Pezzulo, G., Calvi, G., Lalia, D., Ognibene, D.: Fuzzy-based Schema Mechanisms in AKIRA. In: Mohammadian, M. (ed.) Proceedings of CIMCA 2005, Vienna (2005)Google Scholar
  21. 21.
    Pezzulo, G., Calvi, G.: Dynamic Computation and Context Effects in the Hybrid Architecture AKIRA. In: Dey, A.K., Kokinov, B., Leake, D.B., Turner, R. (eds.) CONTEXT 2005. LNCS (LNAI), vol. 3554, pp. 368–381. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  22. 22.
    Tani, J., Nolfi, S.: Learning to perceive the world as articulated: An approach for hierarchical learning in sensory-motor systems. Neural Networks 12, 1131–1141 (1999)CrossRefGoogle Scholar
  23. 23.
    Tyrrell, T.: Computational Mechanisms for Action Selection. PhD thesis, University of Edinburgh (1993)Google Scholar
  24. 24.
    Wolpert, D.M., Kawato, M.: Multiple paired forward and inverse models for motor control. Neural Networks 11(7-8), 1317–1329 (1998)CrossRefGoogle Scholar
  25. 25.
    Yarbus, A.: Eye Movements and Vision. Plenum Press, New York (1967)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Giovanni Pezzulo
    • 1
  • Gianguglielmo Calvi
    • 1
  1. 1.Institute of Cognitive Science and Technology – CNRRomaItaly

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