Abstract
The paper considers supervised learning algorithm of nonlinear perceptron with dynamic targets adjustment which assists in faster learning and cognition. A difference between targets of the perceptron corresponding to objects of the first and second categories is associated with stimulation strength. A feedback chain that controls the difference between targets is interpreted as synthetic emotions. In a population of artificial agents that ought to learn similar pattern classification tasks, presence of the emotions helps a larger fraction of the agents to survive. We found that optimal level of synthetic emotions depends on difficulty of the pattern recognition task and requirements to learning quality and confirm Yerkes-Dodson law found in psychology.
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Ortony, A., Clore, G., Collins, A.: The Cogitive Structure of the Emotions. Cambridge University Press, New York (1988)
Griffiths, P.: What Emotions Really Are: The Problem of Psychological Categories. Chicago University Press, Chicago (1997)
Oatley, K., Jenkins, J.: Understanding Emotions. Blackwell, Oxford (1996)
Izard, C.E.: Four systems for emotion activation – cognitive and noncognitive processes. Psychological Review 100(1), 68–90 (1993)
Lazarus, R.S.: Emotions and adaptation. Oxford University Press, New York (1991)
Goleman, D.: Emotional Intelligence: Why It Can Matter More than IQ. Bloomsbury Publishing, London (1996)
Pfeifer, R.: The fungus eater approach to emotion: a view from artificial intelligence. Cognitive Studies, The Japanese Society for Cognitive Sci. 1, 42–57 (1994)
Chwelos, G., Oatley, K.: Appraisal, computational models, and Scherer expert system. Cognition and Emotion 8(3), 245–257 (1994)
Picard, R.W.: Affective Computing. The MIT Press, Cambridge (1997)
Jacobs, R.A.: Increased rates of convergence through learning rate adaptation. Neural Networks 1(3), 295–307 (1988)
Radi, A., Poli, R.: Genetic programming discovers efficient learning rules for the hidden and output layers of feedforward neural networks. In: Poli, R., Nordin, P. (eds.) EuroGP 1999. LNCS, vol. 1598, pp. 120–134. Springer, Heidelberg (1999)
Yao, X.: Evolving artificial neural networks. Proc. of IEEE 87(1), 1423–1447 (1999)
The MathWorks, Matlab: The language of technical computing (1998), http://www.mathworks.com
Raudys, S.: Classifier’s complexity control while training multilayer perceptrons. In: Amin, A., Pudil, P., Ferri, F., Iñesta, J.M. (eds.) SPR 2000 and SSPR 2000. LNCS, vol. 1876, pp. 32–44. Springer, Heidelberg (2000)
Raudys, S.: Statistical and Neural Classifiers: An integrated approach to design. Springer, Heidelberg (2001)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An introduction. MIT Press, Bradford Book (1998)
Haykin, S.: Neural Networks: A comprehensive foundation, 2nd edn. Prentice-Hall, Englewood Cliffs (1999)
Raudys, S., Justickis, V.: Yerkes-Dodson law in agents’ training. In: Pires, F.M., Abreu, S.P. (eds.) EPIA 2003. LNCS (LNAI), vol. 2902, pp. 54–58. Springer, Heidelberg (2003)
Yerkes, R.M., Dodson, J.D.: The relation of strength of stimulus to rapidity of habit-formation. Journal of Comparative Neurology and Psychology 18, 459–482 (1908)
Teigen, K.H.: Yerkes-Dodson – a law for all seasons. Theory and Psychology 4(4), 525–547 (1994)
Thorndike, E.L.: Animal Intelligence. Hafner, Darien (1911)
Pavlov, I.P.: New researches on conditioned reflexes. Science 58, 359–361 (1923)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In: Rumelhart, D.E., McClelland, J.L. (eds.) Parallel Distributed Processing: Explorations in the microstructure of cognition, vol. I, pp. 318–362. Bradford Books, Cambridge (1986)
Raudys, S.: Evolution and generalization of a single neurone. I. SLP as seven statistical classifiers. Neural Networks 11(2), 283–296 (1998)
Raudys, S.: An adaptation model for simulation of aging process. Int. J. of Modern Physiscs, C. 13(8), 1075–1086 (2002)
Raudys, S., Hussain, A., Justickis, V., Pumputis, A., Augustinaitis, A.: Functional model of criminality: simulation study. In: Dey, A.K., Kokinov, B., Leake, D.B., Turner, R. (eds.) CONTEXT 2005. LNCS (LNAI), vol. 3554, pp. 410–423. Springer, Heidelberg (2005) (in press)
Raudys, S.: Survival of intelligent agents in changing environments. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds.) ICAISC 2004. LNCS (LNAI), vol. 3070, pp. 109–117. Springer, Heidelberg (2004)
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Raudys, Š. (2005). Effect of Synthetic Emotions on Agents’ Learning Speed and Their Survivability. In: Capcarrère, M.S., Freitas, A.A., Bentley, P.J., Johnson, C.G., Timmis, J. (eds) Advances in Artificial Life. ECAL 2005. Lecture Notes in Computer Science(), vol 3630. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11553090_1
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DOI: https://doi.org/10.1007/11553090_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28848-0
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