Abstract
Applying methods of artificial evolution to synthesize robot controllers for complex tasks is still a challenging endeavor. We report an approach which might have the potential to improve the performance of evolutionary algorithms in the context of evolutionary robotics. We apply a controller concept that is inspired by signaling networks found in nature. The implementation of spatial features is based on Voronoi diagrams that describe a compartmentalization of the agent’s inner body. These compartments establish a virtual embodiment, including sensors and actuators, and influence the dynamics of virtual hormones. We report results for an exploring task and an object discrimination task. These results indicate that the controller, that determines the principle hormone dynamics, can successfully be evolved in parallel with the compartmentalizations, that determine the spatial features of the sensors, actuators, and hormones.
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References
Alberts, B.: Molecular biology of the cell. Garland Pub. (1989)
Aurenhammer, F.: Voronoi diagrams — a survey of a fundamental geometric data structure. ACM Computing Surveys 23(3), 345–405 (1991)
Beer, R.D.: The dynamics of active categorical perception in an evolved model agent. Adaptive Behavior 11(4), 209–243 (2003)
Beer, R.D., Gallagher, J.C.: Evolving dynamical neural networks for adaptive behavior. Adaptive Behavior 1(1), 91–122 (1992)
Bray, D.: Wetware: A Computer in Every Living Cell. Yale University Press (2009)
Dale, K., Husbands, P.: The evolution of reaction-diffusion controllers for minimally cognitive agents. Artificial Life 16(1), 1–19 (2010)
Hamann, H., Schmickl, T., Crailsheim, K.: A hormone-based controller for evaluation-minimal evolution in decentrally controlled systems. Artificial Life 18(2), 165–198 (2012)
Lodish, H., Berk, A., Zipursky, L.S., Matsudaira, P., Baltimore, D., Darnell, J.E.: Molecular Cell Biology, 5th edn. W.H. Freeman and Company (2003)
Moioli, R., Vargas, P.A., Husbands, P.: Exploring the kuramoto model of coupled oscillators in minimally cognitive evolutionary robotics tasks. In: WCCI 2010 IEEE World Congress on Computational Intelligence - CEC IEEE, pp. 2483–2490 (2010)
Nelson, A.L., Barlow, G.J., Doitsidis, L.: Fitness functions in evolutionary robotics: A survey and analysis. Robotics and Autonomous Systems 57, 345–370 (2009)
Nolfi, S., Floreano, D.: Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines. MIT Press (2000)
Schmickl, T., Hamann, H., Crailsheim, K.: Modelling a hormone-inspired controller for individual- and multi-modular robotic systems. Mathematical and Computer Modelling of Dynamical Systems 17(3), 221–242 (2011)
Schmickl, T., Hamann, H., Stradner, J., Crailsheim, K.: Hormone-based control for multi-modular robotics. In: Levi, P., et al. (eds.) Symbiotic Multi-Robot Organisms: Reliability, Adaptability, Evolution, pp. 240–263. Springer (2010)
Schoenauer, M., Kallel, L., Jouve, F.: Mechanical inclusions identification by evolutionary computation (1996)
Stradner, J., Hamann, H., Schmickl, T., Crailsheim, K.: Analysis and implementation of an artificial homeostatic hormone system: A first case study in robotic hardware. In: The 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2009), pp. 595–600. IEEE Press (2009)
Voronoi, G.: Nouvelles applications des paramétres continus à la théorie des formes quadratiques. Journal für Reine und Angewandte Mathematik 133, 97–178 (1907)
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Stradner, J., Hamann, H., Schwarzer, C.S.F., Michiels, N.K., Schmickl, T. (2013). Virtual Spatiality in Agent Controllers: Encoding Compartmentalization. In: Esparcia-Alcázar, A.I. (eds) Applications of Evolutionary Computation. EvoApplications 2013. Lecture Notes in Computer Science, vol 7835. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37192-9_58
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DOI: https://doi.org/10.1007/978-3-642-37192-9_58
Publisher Name: Springer, Berlin, Heidelberg
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