Abstract
The development of an effective central model of action selection has already been reviewed in previous work. The central model has been set to resolve a foraging task with the use of heterogeneous behavioral modules. In contrast to collecting/depositing modules that have been hand-coded, modules related to exploring follow an evolutionary approach. However, in this paper we focus on the use of genetic algorithms for evolving the weights related to calculating the urgency for a behavior to be selected. Therefore, we aim to reduce the number of decisions made by a human designer when developing the neural substratum of a central selection mechanism.
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References
Maes, P.: How to do the right thing. Connection Science Journal 1(3), 291–323 (1989)
Nolfi, S.: Evolving non-trivial behaviors on real robots: A garbage collection robot. Robotics and automation system 22, 187–198 (1997)
Nolfi, S., Floreano, D., Miglino, O., Mondada, F.: How to evolve autonomous robots: Different approaches in evolutionary robotics. In: Proceedings of the International Conference Artificial Life IV, MIT Press, Cambridge MA (1994)
Seth, A.K.: Evolving action selection and selective attention without actions, attention, or selection. In: From animals to animats 5: Proceedings of the Fifth International Conference on the Simulation of Adaptive Behavior, pp. 139–147. MIT Press, Cambridge (1998)
Kyong-Joong Kim, S.-B.C.: Robot action selection for higher behaviors with cam-brain modules. In: Proceedings of the 32nd ISR (International Symposium in Robotics) (2001)
Yamauchi, B.B.R.: Integrating reactive, sequential, and learning behavior using dynamical neural networks. In: From Animals to Animats 3, Proceedings of the 3rd International Conference on Simulation of Adaptive Behavior, MIT Press/Bradford Books (1994)
Holland, J.H.: Adaptation in natural and artificial systems. MIT Press, Cambridge (1992)
Floreano, D., Mondana, F.: Evolution of homing navigation in a real mobile robot. IEEE Transactions on Systems, Man and Cybernetics 26(3), 396–407 (1996)
Nolfi, S., Floreano, D.: Evolutionary robotics. MIT Press, Cambridge (2000)
Santos, J., Duro, R.: Artificial evolution and autonomus robotics (in spanish). Ra-Ma Editorial (2005)
Mondana, F., Franzi, E., I.P.: Mobile robot miniaturisation: A tool for investigating in control algorithms. In: Proceedings of the 3rd International Symposium on Experimental Robotics, pp. 501–513. Springer, Heidelberg (1993)
Webots, Commercial Mobile Robot Simulation Software (2006), http://www.cyberbotics.com
Floreano, D., Mondana, F.: Automatic creation of an autonomous agent: Genetic evolution of a neural-network driven robot. In: From Animals to Animats III: Proceedings of the Third International Conference on Simulation of Adaptive Behavior, MIT Press-Bradford Books, Cambridge (1994)
Bajaj, D., Ang Jr., M.H.: An incremental approach in evolving robot behavior. In: The Sixth International Conference on Control, Automation, Robotics and Vision (ICARCV 2000) (2000)
Montes González, F.M., Marín Hernández, A., Ríos Figueroa, H.: An effective robotic model of action selection. In: Marín, R., Onaindía, E., Bugarín, A., Santos, J. (eds.) CAEPIA 2005. LNCS (LNAI), vol. 4177, pp. 123–132. Springer, Heidelberg (2006)
Nolfi, S.: Using emergent modularity to develop control systems for mobile robots. Adaptive Behavior 5(3/4), 343–363 (1997)
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Montes-González, F., Reyes, J.S., Figueroa, H.R. (2006). Integration of Evolution with a Robot Action Selection Model. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_111
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DOI: https://doi.org/10.1007/11925231_111
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