Abstract
A prospective paradigm, named Growing Functional Modules (GFM) has been introduced in a recent publication. Based on the epigenetic approach, the GFM paradigm is conceived to automatically generate "artificial brains" that are able to build, through interaction, their own representation of their environments. The present application consists in designing an artificial brain for a simple virtual mushroom shaped robot named hOnGo. This paper describes this initial implementation and its preliminary results.
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References
Piaget, J.: Genetic Epistemology (Series of Lectures). Columbia University Press, Columbia (1970)
Prince, G., Demiris, Y.: Introduction to the Special Issue on Epigenetic Robotics. International Society for Adaptative Behavior 11(2), 75–77 (2003)
Brooks, R.: Intelligence without Representation. Artificial Intelligence 47, 139–159 (1991)
Grupen, A.: A Developmental Organization for Robot Behavior. In: Proceedings of the third International Workshop on Epigenetic Robotics. IEEE, Massachusetts USA (2003)
Berthouze, L., Prince, G.: Introduction: the Third International Workshop on Epigenetic Robotics. In: Prince, C.G., Berthouze, L., Kozima, H., Bullock, D., Stojanov, G., Balkenius, C. (eds.) Proceedings of the Third International Workshop on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems, Lund Sweden. Lund University Cognitive Studies, vol. 101 (2003)
Lindblom, J., Ziemke, T.: Social Situatedness: Vygotsky and Beyond. In: Prince, C.G., Demiris, Y., Marom, Y., Kozima, H., Balkenius, C. (eds.) Proceedings of the Second International Workshop on Epigenetic Robotics, Modeling Cognitive Development in Robotic Systems 1994, Edinburgh, Scotland, pp. 71–78 (2002)
Leboeuf, J.: Growing Functional Modules, a Prospective Paradigm for Epigenetic Artificial Intelligence. In: Ramos, F.F., Larios Rosillo, V., Unger, H. (eds.) ISSADS 2005. LNCS, vol. 3563, pp. 465–471. Springer, Heidelberg (2005)
Leboeuf, J.: A Self-Developing Neural Network Designed for Discrete Event System Autonomous Control. In: Mastorakis, N. (ed.) Advances in Systems Engineering, Signal Processing and Communications, pp. 30–34. Wseas Press (2002)
Leboeuf, J.: Facing Combinatory Explosion in NAC Networks. Advanced Distributed Systems. In: Ramos, F.F., Unger, H., Larios, V. (eds.) ISSADS 2004. LNCS, vol. 3061, pp. 252–260. Springer, Heidelberg (2004)
Leboeuf, J.: NAC, an Artificial Brain for Epigenetic Robotics. In: Jamshidi, O., de Dios, M. (eds.) Robotics: trends, principles and applications. TSI Press Series, pp. 535–540 (2004)
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Pasquier, J.L. (2005). Applying the GFM Prospective Paradigm to the Autonomous and Adaptative Control of a Virtual Robot. In: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds) MICAI 2005: Advances in Artificial Intelligence. MICAI 2005. Lecture Notes in Computer Science(), vol 3789. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11579427_98
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DOI: https://doi.org/10.1007/11579427_98
Publisher Name: Springer, Berlin, Heidelberg
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