Abstract
Various complex systems have an underlying architecture governed by shared organizing principles. We model the natural perceptual schema as a complex system by introducing a hidden variable, called negative schema, based on the idea of Minsky’s negative knowledge. We first show that the negative schema is necessary in recognizing a negated concept and that the positive schema is defined only relative to the negative schema. In the paper, we claim that the schema shares the same underlying architecture of complex systems by proving that the positive and the negative schemas inside the system interact with each other simultaneously and in parallel, and that the suppressor controls the interaction for emerging property.
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References
Barabasi, A.-L., Bonabeau, E.: Scale-Free Networks. Sci. Amer. 288(5) (2005)
Cohen, P.R., Atkin, M.S., Oates, T.: Neo: Learning Conceptual Knowledge by Sensorimotor Interaction with an Environment. In: Proceedings of the 6th International Conference on Intelligent Autonomous System (2000)
Cummins, R., Cummins, D.D.: Minds, Brains, and Computers: The Foundations of Cognitive Science. Blackwell, Oxford (2000)
Dooley, K., Corman, S.: Agent-based, genetic, and emergent computational models of complex systems. In: Kiel, L.D. (ed.) Encyclopedia of Life Support Systems (EOLSS). UNESCO/EOLSS Publishers, Oxford, U.K. (2002)
Endy, D.: Foundations for engineering biology. Nature, 424–438 (November 2005)
Fodor, J.A.: Precis of the Modularity of Mind, Behavioral and Brain Science (1985)
Minsky, M.: Negative Expertise. International Journal of Expert Systems 7(1) (1994)
Minsky, M.: Future Models for Mind-Machines, http://www.media.mit.edu/people/minsky
Shavlik, J.W., Dietterich, T.G.: Readings in Machine Learning. Morgan KaufMann, San Francisco (1990)
Sowa, J.F.: Conceptual Structures: Information Processing in Mind and Machine. Addison-Wesley Publishing Co., Reading (1983)
Tae, K.S.: Semantic Aspects of Negation as Schema. Journal of Korea Information Processing Society 9-B (1) (2002)
Yaneer, B.: Dynamics of Complex Systems. Addison-Wesley, Reading (1997)
Weiser, M.: The Computer for the Twenty-First Century. Scientific Am. 265(3), 6–75 (1991)
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© 2007 Springer-Verlag Berlin Heidelberg
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Tae, K.S., Jeong, A.R., You, K.S. (2007). Cognitive Model of Schema as Complex System. In: Gervasi, O., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2007. ICCSA 2007. Lecture Notes in Computer Science, vol 4706. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74477-1_38
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DOI: https://doi.org/10.1007/978-3-540-74477-1_38
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74475-7
Online ISBN: 978-3-540-74477-1
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