Abstract
In order to survive in an unpredictable and changing environment, an agent has to continuously make sense and adapt to the incoming sensory information and extract those that are behaviorally relevant. At the same time, it has to be able to learn to associate specific actions to these different percepts through reinforcement. Using the biologically grounded Distributed Adaptive Control (DAC) robot-based neuronal model, we have previously shown how these two learning mechanisms (perceptual and behavioral) should not be considered separately but are tightly coupled and interact synergistically via the environment. Through the use of a simulated setup and the unified framework of the DAC architecture, which offers a pedagogical model of the phases that form a learning process, we aim to analyze this perceptual-behavioral binomial and its effects on learning.
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References
Marcos, E., Sánchez-Fibla, M., Verschure, P.F.M.J.: The complementary roles of allostatic and contextual control systems in foraging tasks. In: Doncieux, S., Girard, B., Guillot, A., Hallam, J., Meyer, J.-A., Mouret, J.-B. (eds.) SAB 2010. LNCS(LNAI), vol. 6226, pp. 370–379. Springer, Heidelberg (2010)
Duff, A.: The dynamics of perceptual and behavioral learning in executive control. Ph.D. thesis, Diss., Eidgenössische Technische Hochschule ETH Zürich, Nr. 18766, 2009 (2009)
Verschure, P.F.M.J., Voegtlin, T., Douglas, R.J.: Environmentally mediated synergy between perception and behaviour in mobile robots. Nature 425(6958), 620–624 (2003)
Verschure, P.F.M.J.: Distributed adaptive control: a theory of the mind, brain, body nexus. Biologically Inspired Cogn. Architectures 1, 55–72 (2012)
Kruger, J., Dunning, D.: Unskilled and unaware of it: how difficulties in recognizing one’s own incompetence lead to inflated self-assessments. J. Pers. Soc. Psychol. 77(6), 1121 (1999)
Duff, A., Verschure, P.F.M.J.: Unifying perceptual and behavioral learning with a correlative subspace learning rule. Neurocomputing 73(10–12), 1818–1830 (2010)
Koenig, N., Howard, A.: Design and use paradigms for gazebo, an open-source multi-robot simulator. In: Proceedings. 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems, (IROS 2004). vol. 3, pp. 2149–2154. IEEE (2004)
Bernardet, U., Verschure, P.F.M.J.: iqr: A tool for the construction of multi-level simulations of brain and behaviour. Neuroinformatics 8(2), 113–134 (2010)
Piaget, J.: The Origins of Intelligence in Children, vol. 8(5), pp. 18–1952. International Universities Press, New York (1952)
Peirce, C.S., Turrisi, P.A.: Pragmatism as a Principle and Method of Right Thinking: The 1903 Harvard Lectures on Pragmatism. SUNY Press, New York (1997)
Acknowledgments
This work is supported by the EU FP7 project WYSIWYD (FP7-ICT-612139) and EASEL (FP7-ICT- 611971).
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Blancas, M., Zucca, R., Vouloutsi, V., Verschure, P.F.M.J. (2016). Modulating Learning Through Expectation in a Simulated Robotic Setup. In: Lepora, N., Mura, A., Mangan, M., Verschure, P., Desmulliez, M., Prescott, T. (eds) Biomimetic and Biohybrid Systems. Living Machines 2016. Lecture Notes in Computer Science(), vol 9793. Springer, Cham. https://doi.org/10.1007/978-3-319-42417-0_37
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DOI: https://doi.org/10.1007/978-3-319-42417-0_37
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