Abstract
This paper describes a novel organizational learning model for multiple adaptive robots. In this model, robots acquire their own appropriate functions through local interactions among their neighbors, and get out of deadlock situations without explicit control mechanisms or communication methods. Robots also complete given tasks by forming an organizational structure, and improve their organizational performance. We focus on the emergent processes of collective behaviors in multiple robots, and discuss how to control these behaviors with only local evaluation functions, rather than with a centralized control system. Intensive simulations of truss construction by multiple robots gave the following experimental results: (1) robots in our model acquire their own appropriate functions and get out of deadlock situations without explicit control mechanisms or communication methods; (2) robots form an organizational structure which completes given tasks in fewer steps than are needed with a centralized control mechanism.
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References
Sugihara K, Suzuki I (1990) Distributed motion coordination of multiple mobile robots. IEEE International Symposium on Intelligent Control, pp 138–143
Mataric MJ (1994) Learning to behave socially. 3rd International Conference on Simulation of Adaptive Behavior (From Animals to Animats, 3), pp 453–462
Parker LE (1994) ALLIANCE: an architecture for fault tolerant, cooperative control of heterogeneous mobile robots. IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'94), pp 776–783
Asama K, Ozaki H, Itakura A, Matsumoto A, Ishida Y, Endo I (1991) Collision avoidance among multiple mobile robots based on rules and communication. IEEE International Workshop on Intelligent Robots and Systems '91, pp 1215–1220
Yuta S, Premvuti S (1992) Coordinating autonomous and centralized decision making to active cooperative behaviors between multiple mobile robots. IEEE International Workshop on Intelligent Robots and Systems '92, pp 1566–1575
Argyris C, Schon DA (1978) Organizational learning. Addison-Wesley, Reading
Duncan R, Weiss A (1979) Organizational learning: implications for organizational design. Res Organ Behav 1:75–123
Espejo R, Schuhmann W, Schwaninger M, Bilello U (1996) Organizational transformation and learning. Wiley, New York
March JG (1991) Exploration and exploitation in organizational learning. Organ Sci 2: 71–87
Takadama K, Terano T (1997) Good solutions will emerge without a global objective function: applying organizational classifier system to printed circuit board design. IEEE International Conference on Systems, Man, and Cybernetics (SMC'97), pp 3355–3360
Takadama K, Nakasuka S, Terano T (1998) Printed circuit board design via organizational-learning agents. Applied intelligence: special issue on intelligent adaptive agents, in press
Takadama K, Hajiri K, Nomura T, Okada M, Shimohara K, Nakasuka S (1997) A computational group dialogue model with organizational learning. IEEE 1997 International Conference on Intelligent Processing Systems (ICIPS '97), pp 174–179
Takadama K, Hajiri K, Nomura T, Nakasuka S, Shimohara K (1998) Organizational knowledge on formation in multiple robots learning. 3nd International Symposium on Artificial Life and Robotics (AROB'98), pp 397–401
Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley, Reading
Holland JH (1985) Properties of the bucket brigade algorithm. 1st International Conference on Genetic Algorithms (ICGA '85), pp 1–7
Grefenstette JJ (1988) Credit assignment in rule discovery systems based on genetic algorithms. Mach Learn 3:225–245
Takadama K, Hajiri K, Nomura T, Nakasuka S, Shimohara K (1998) Reinforcement learning for multiple robots with organizational learning. 3rd International Symposium on Artificial Life and Robotics (AROB'98), pp 392–396
Takadama K, Nakasuka S, Terano T (1998) Multiagent reinforcement learning with organizational-learning-oriented classifier system. IEEE 1998 International Conference on Evolutionary Computation (ICEC'98), pp 63–68
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Takadama, K., Hajiri, K., Nomura, T. et al. Learning model for adaptive behaviors as an organized group of swarm robots. Artificial Life and Robotics 2, 123–128 (1998). https://doi.org/10.1007/BF02471168
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DOI: https://doi.org/10.1007/BF02471168