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Abstraction Level Regulation of Cognitive Processing Through Emotion-Based Attention Mechanisms

  • Luís Morgado
  • Graça Gaspar
Conference paper
  • 1.2k Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4840)

Abstract

In domains where time and resources are limited, the ability to balance resource consumption according to the problem characteristics and to the required solution quality is a crucial aspect of intelligent behavior. Growing evidence indicates that emotional phenomena may play an important role in that balance. To support this view we propose an agent model where emotion and reasoning are conceived as two symbiotically integrated aspects of cognitive processing. In this paper we concretize this view by extending emotion-based regulation of cognitive activity to enable an active control of the abstraction level at which cognitive processes operate through emotion-based attention mechanisms, thus allowing a dynamical adjustment of the resources used. Experimental results are presented to illustrate the proposed approach and to evaluate its effectiveness in a scenario where reasoning under time-limited conditions in a dynamic environment is required.

Keywords

Cognitive Processing Cognitive Activity Abstraction Level Dissipative Structure Emotional Disposition 
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.
    Simon, H.: Motivational and Emotional Controls of Cognition. Psychological Review 74 (1967)Google Scholar
  2. 2.
    Damásio, A.: A Second Chance for Emotion. In: Lane, R., Nadel, L. (eds.) Cognitive Neuroscience of Emotion, Oxford Univ. Press, Oxford (2000)Google Scholar
  3. 3.
    Gray, J., Braver, T., Raichle, M.: Integration of Emotion and Cognition in the Lateral Prefrontal Cortex. Proceedings of the National Academy of Sciences, USA (2002)Google Scholar
  4. 4.
    Anderson, M., Oates, T(eds.): Metacognition in Computation, AAAI Spring Symposium, Technical Report SS-05-04, AAAI Press (2005)Google Scholar
  5. 5.
    Gigerenzer, G., Selten, R. (eds.): Bounded Rationality: The Adaptive Toolbox. MIT Press, Cambridge (1999)Google Scholar
  6. 6.
    Cañamero, L.: Designing Emotions for Activity Selection in Autonomous Agents. In: Trappl, R. (ed.) Emotions in Humans and Artifacts, MIT Press, Cambridge (2000)Google Scholar
  7. 7.
    Almeida, L., Silva, B., Bazzan, A.: Towards a physiological model of emotions: first steps, Hudlicka, E., Cañamero, L. (eds.) AAAI Spring Symposium, Technical Report SS-04-02 (2004)Google Scholar
  8. 8.
    Ortony, A., Clore, G., Collins, A.: The Cognitive Structure of Emotions. Cambridge University Press, Cambridge (1988)CrossRefGoogle Scholar
  9. 9.
    Gratch, J., Marsella, S.: Evaluating a General Model of Emotional Appraisal and Coping, Hudlicka, E., Cañamero, L. (eds.) AAAI Spring Symposium, Tech. Rep. SS-04-02 (2004)Google Scholar
  10. 10.
    Hudlicka, E.: Modeling Interaction between Metacognition and Emotion in a Cognitive Architecture, AAAI Spring Symposium, Technical Report SS-05-04, AAAI Press (2005)Google Scholar
  11. 11.
    Munos, R., Moore, A.: Variable Resolution Discretization in Optimal Control. Machine Learning 1, 1–31 (2001)zbMATHGoogle Scholar
  12. 12.
    Schoknecht, R., Riedmiller, M.: Learning to Control at Multiple Time Scales. In: Kaynak, O., Alpaydın, E., Oja, E., Xu, L. (eds.) ICANN 2003 and ICONIP 2003. LNCS, vol. 2714, Springer, Heidelberg (2003)Google Scholar
  13. 13.
    Sturtevant, N., Buro, M.: Partial Pathfinding Using Map Abstraction and Refinement. In: Proc. of the International Joint Conference on Artificial Intelligence, AAAI Press, Stanford, California, USA (2005)Google Scholar
  14. 14.
    Scherer, K.: Emotions as Episodes of Subsystem Synchronization Driven by Nonlinear Appraisal Processes. In: Lewis, M., Granic, I. (eds.) Emotion, Development, and Self-Organization, Cambridge Univ. Press, Cambridge (2000)Google Scholar
  15. 15.
    Carver, C., Scheier, M.: Control Processes and Self-organization as Complementary Principles Underlying Behavior. In: Pers. and Social Psych. Review (2002)Google Scholar
  16. 16.
    Kondepudi, D., Prigogine, I.: Modern Thermodynamics: From Heat Engines to Dissipative Structures. J. Wiley & Sons, Chichester (1998)zbMATHGoogle Scholar
  17. 17.
    Morgado, L., Gaspar, G.: Emotion in Intelligent Virtual Agents: The Flow Model of Emotion. In: Rist, T., Aylett, R., Ballin, D., Rickel, J. (eds.) IVA 2003. LNCS (LNAI), vol. 2792, Springer, Heidelberg (2003)CrossRefGoogle Scholar
  18. 18.
    Gärdenfors, P.: Conceptual Spaces: The Geometry of Thought. MIT Press, Cambridge (2000)Google Scholar
  19. 19.
    Moore, S., Oaksford, M. (eds.): Emotional Cognition. John Benjamins Press, Amsterdam (2002)Google Scholar
  20. 20.
    Morgado, L., Gaspar, G.: Emotion Based Adaptive Reasoning for Resource Bounded Agents. In: Proc. 4th Int. Joint Conference on Autonomous Agents and Multi-Agent Systems, ACM Press, New York (2005)Google Scholar
  21. 21.
    Coren, S., Ward, L., Enns, J.: Sensation and Perception. Wiley, Chichester (2004)Google Scholar
  22. 22.
    Ghallab, M., Nau, D., Traverso, P.: Automated Planning. Morgan Kaufmann, San Francisco (2004)zbMATHGoogle Scholar
  23. 23.
    Kinny, D., Georgeff, M.: Commitment and Effectiveness of Situated Agents. In: Proc. of the 12th International Joint Conference on Artificial Intelligence (1991)Google Scholar
  24. 24.
    Schut, M., Wooldridge, M., Parsons, S.: The Theory and Practice of Intention Reconsideration. J. Expt. Theor. Artificial Intelligence 16(4) (2004)Google Scholar
  25. 25.
    Koenig, S., Likhachev, M.: RealTime Adaptive A*. In: Proc. 4th Int. Joint Conference on Autonomous Agents and Multi-Agent Systems (2006)Google Scholar
  26. 26.
    Horvitz, E., Zilberstein, S.: Computational Tradeoffs under Bounded Resources. Artificial Intelligence Journal 126 (2001)Google Scholar
  27. 27.
    Lindblom, J., Ziemke, T.: The Social Body in Motion: Cognitive Development in Infants and Androids. Connection Science 18(4) (2006)Google Scholar
  28. 28.
    Morgado, L., Gaspar, G.: Adaptation and Decision-Making Driven by Emotional Memories. In: Bento, C., Cardoso, A., Dias, G. (eds.) EPIA 2005. LNCS (LNAI), vol. 3808, Springer, Heidelberg (2005)CrossRefGoogle Scholar
  29. 29.
    Staddon, J.: Adaptive Dynamics: The Theoretical Analysis of Behavior. MIT Press, Cambridge (2001)Google Scholar
  30. 30.
    Bergareche, A., Ruiz-Mirazo, K.: Metabolism and the Problem of its Universalization. Biosystems 49 (1999)Google Scholar
  31. 31.
    Nicolis, G., Prigogine, I.: Self-Organization in Nonequilibrium Systems: From Dissipative Structures to Order trough Fluctuations. John Wiley & Sons, Chichester (1977)zbMATHGoogle Scholar
  32. 32.
    Angrilli, A., Cherubini, P., Pavese, A.: The Influence of affective Factors on Time Perception. Perception & Psychophysics 59(6) (1997)Google Scholar
  33. 33.
    Morgado, L., Gaspar, G.: Emotion in Intelligent Virtual Agents: The Flow Model of Emotion. In: Rist, T., Aylett, R., Ballin, D., Rickel, J. (eds.) IVA 2003. LNCS (LNAI), vol. 2792, pp. 31–38. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Luís Morgado
    • 1
    • 2
  • Graça Gaspar
    • 2
  1. 1.Instituto Superior de Engenharia de Lisboa, Rua Conselheiro Emídio Navarro, 1949-014 LisboaPortugal
  2. 2.Faculdade de Ciências da Universidade de Lisboa, Universidade de Lisboa, Campo Grande, 1749-016 LisboaPortugal

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