Abstract
Classical conditioning is important in humans to learn and predict events in terms of associations between stimuli and to produce responses based on these associations. Social robots that have a classical conditioning skill like humans will have an advantage to interact with people more naturally, socially and effectively. In this paper, we present a novel classical conditioning mechanism and describe its implementation in ASMO cognitive architecture. The capability of this mechanism is demonstrated in the Smokey robot companion experiment. Results show that Smokey can associate stimuli and predict events in its surroundings. ASMO’s classical conditioning mechanism can be used in social robots to adapt to the environment and to improve the robots’ performances.
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References
Bishop, C.: Pattern Recognition and Machine Learning. Information Science and Statistics. Springer (2006)
Chance, P.: Learning and behavior. Wadsworth (2013)
Furze, T.A.: The application of classical conditioning to the machine learning of a commonsense knowledge of visual events. Ph.d. dissertation, University of Leeds, United Kingdom (2013)
Novianto, R.: Flexible Attention-based Cognitive Architecture for Robots. Ph.d. dissertation, University of Technology Sydney, Sydney, Australia (2014)
Novianto, R., Johnston, B., Williams, M.A.: Attention in the ASMO cognitive architecture. In: Samsonovich, A.V., Jóhannsdóttir, K.R., Chella, A., Goertzel, B. (eds.) Proceedings of the First Annual Meeting of the BICA Society. Frontiers in Artificial Intelligence and Applications, vol. 221, pp. 98–105. IOS Press (November 2010)
Novianto, R., Johnston, B., Williams, M.-A.: Habituation and sensitisation learning in ASMO cognitive architecture. In: Herrmann, G., Pearson, M.J., Lenz, A., Bremner, P., Spiers, A., Leonards, U. (eds.) ICSR 2013. LNCS, vol. 8239, pp. 249–259. Springer, Heidelberg (2013)
Novianto, R., Williams, M.A.: The role of attention in robot self-awareness. In: The 18th IEEE International Symposium on Robot and Human Interactive Communication, RO-MAN 2009, pp. 1047–1053 (2009)
Novianto, R., Williams, M.A.: Innate and learned emotion network. In: Samsonovich, A.V., Jóhannsdóttir, K.R. (eds.) Proceedings of the Second Annual Meeting of the BICA Society. Frontiers in Artificial Intelligence and Applications, vol. 233, pp. 263–268. IOS Press (November 2011)
Pavlov, I., Anrep, G.: Conditioned Reflexes. Dover Publications (2003)
Powell, R.A., Honey, P.L., Symbaluk, D.G.: Introduction to learning and behavior, 4th edn. Cengage Learning (2013)
Russell, S.J., Norvig, P.: Artificial intelligence: a modern approach. Prentice Hall series in artificial intelligence. Prentice Hall (2010)
Sutton, R.S., Barto, A.G.: Toward a modern theory of adaptive networks: expectation and prediction. Psychological Review 88(2), 135–170 (1981)
Sutton, R.S., Barto, A.G.: Time-derivative models of pavlovian reinforcement. In: Gabriel, M., Moore, J. (eds.) Learning and Computational Neuroscience: Foundations of Adaptive Networks, pp. 497–537. The MIT Press, Cambridge (1990)
Wagner, A.R.: Sop: A model of automatic memory processing in animal behavior. In: Spear, N.E., Miller, R.R. (eds.) Information Processing in Animals: Memory Mechanisms, pp. 5–47. ch. 1. Lawrence Erlbaum Associates, Hillsdale (1981)
Williams, M.-A.: Robot social intelligence. In: Ge, S.S., Khatib, O., Cabibihan, J.-J., Simmons, R., Williams, M.-A. (eds.) ICSR 2012. LNCS, vol. 7621, pp. 45–55. Springer, Heidelberg (2012)
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Novianto, R., Williams, MA., Gärdenfors, P., Wightwick, G. (2014). Classical Conditioning in Social Robots. In: Beetz, M., Johnston, B., Williams, MA. (eds) Social Robotics. ICSR 2014. Lecture Notes in Computer Science(), vol 8755. Springer, Cham. https://doi.org/10.1007/978-3-319-11973-1_29
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DOI: https://doi.org/10.1007/978-3-319-11973-1_29
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