Abstract
In reactive layers of robotic architectures, behaviors should learn their operation from experience, following the trends of modern intelligence theories. A Case Based Reasoning (CBR) reactive layer could achieve this goal but, as complexity of behaviors increases, the curse of dimensionality arises:too many cases in the behaviors casebases degrade response times so robot’s reactiveness is finally too slow for a good performance. In this work we analyze this problem and propose some improvements in the traditional CBR structure and retrieval phase, at reactive level, to reduce the impact of scalability problems when facing complex behaviors design.
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References
Aamodt, A., Plaza, E.: Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications 7(1), 39–52 (1994)
Aguirre, E., González, A.: Fuzzy behaviors for mobile robot navigation: design, coordination and fusion. Int. J. Approx. Reason. 25(3), 255–289 (2000)
Brooks, R.: A robust layered control system for a mobile robot. IEEE J. Robot Automat. 2(1), 14–23 (1986)
Garner, W.R.: The processing of information and structure. Halsted Press, The Experimental Psychology Series. L. Erlbaum Ass. (1974)
Hawkins, J., Blakeslee, S.: On Intelligence. Owl Books (2004)
Khatib, O.: Real-time obstacle avoidance for manipulators and mobile robots. In: IEEE 1985 Int. Conf. Robot, vol. 2, pp. 500–505 (1985)
Kruusmaa, M.: Global navigation in dynamic environments using case-based reasoning. Autonomous Robots 14(1), 71–91 (2003)
Low, K.H., Leow, W.K., Ang Jr., M.H.: A hybrid mobile robot architecture with integrated planning and control. In: Int. J. Conf. Auton. Agent Multi. Ag. (AAMAS 2002), pp. 219–226, NY, USA (2002)
Mataric, M.J.: Interaction and Intelligent Behavior. PhD thesis, Department of Electronic Engineering and Computer Sciencie (1994)
Murphy, R.: Introduction to AI Robotics. MIT Press, Cambridge (2000)
Murray, J.C., Erwin, H.R.: Wermter., S.: Robotic sound-source localization architecture usingcross-correlation and recurrent neural networks. Neural Networks 22(2), 173–189 (2009)
Peula, J.M., Urdiales, C., Herrero, I., Sánchez-Tato, I., Sandoval, F.: Pure reactive behavior learning using case based reasoning for a vision based 4-legged robot. Robot. Auton. Syst. 57(6–7), 688–699 (2009)
Ros, Raquel, López de Màntaras, Ramon, Arcos, Josep-Lluís, Veloso, Manuela M.: Team Playing Behavior in Robot Soccer: A Case-Based Reasoning Approach. In: Weber, Rosina O., Richter, Michael M. (eds.) ICCBR 2007. LNCS (LNAI), vol. 4626, pp. 46–60. Springer, Heidelberg (2007)
Wang, M., Liu, J.N.K.: Fuzzy logic-based real-time robot navigation in unknown environment with dead ends. Robot. Auton. Syst. 56(7), 625–643 (2008)
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Herrero, I., Urdiales, C., Peula, J.M., Sandoval, F. (2015). A Bottom-up Robot Architecture Based on Learnt Behaviors Driven Design. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2015. Lecture Notes in Computer Science(), vol 9094. Springer, Cham. https://doi.org/10.1007/978-3-319-19258-1_14
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DOI: https://doi.org/10.1007/978-3-319-19258-1_14
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