Share / Export Citation / Email / Print / Text size:

International Journal on Smart Sensing and Intelligent Systems

Subject: Computational Science & Engineering, Engineering, Electrical & Electronic


eISSN: 1178-5608




VOLUME 4 , ISSUE 4 (December 2011) > List of articles


Naoto Hoshikawa / Masahiro Ohka / Hanafiah Bin Yussof

Keywords : Multi-agent, Bottom-up approach, Collective task, Multi-fingered hand, Evolution, Behavior table, Genetic algorithm.

Citation Information : International Journal on Smart Sensing and Intelligent Systems. Volume 4, Issue 4, Pages 583-606, DOI:

License : (CC BY-NC-ND 4.0)

Received Date : 23-October-2011 / Accepted: 10-November-2011 / Published Online: 01-December-2011



While the top-down approach of artificial intelligence encounters the frame problem, the bottom-up approach based on a creature’s evolution and behavior is effective for robotic design of intellectual behavior in a specific field. We propose the Evolutionary Behavior Table System (EBTS) using a simple genetic algorithm (SGA) to acquire the autonomous cooperative behavior of multi-agents as the bottom-up approach. In EBTS, a set of rules is expressed as a table composed of sensor input columns and actuator output columns; a row of the table corresponds to a rule. Since each rule is transformed to a string of Boolean values, we treat a long string composed of actuator output strings in the rules as a gene to obtain an optimum gene that adapts to the environment using SGA. In computational experiments, the collective robots could convey an object to a goal through cooperative work; the multi-fingered hands grasped the object and transferred it to the goal. Final truth tables obtained by the gene data do not always assure an optimum solution, but the calculation cost is reduced from astronomical figures to around one ten to twenty thousandth. If we use the top-down methodology, astronomical trials are needed to specify the optimum pattern. Therefore, EBTS is an attractive method because it is very useful for obtaining general robotic behaviors in both collective and multi-fingered hand tasks.

Content not available PDF Share



[2] G. Dudek & M. Jenkin, Computational Principles of Mobile Robot (Second Edition), Cambridge University Press, 2010.
[5] T. Shibata, T. Mitsui, K. Wada and K. Tanie, “Subjective Evaluation of Seal Robot: Paro”, Journal of Robotics and Mechatronics, Vol.14, No.1, pp.13-19, 2002.
[6] M. Ohka, N. Hoshikawa, J. Wada, and H. B. Yussof, “Two Methodologies Toward Artificial Tactile Affordance System in Robotics”, International Journal on Smart Sensing and Intelligent Systems, Vol. 3, No. 3, pp.466-487, 2010.
[7] N. Hoshikawa & M. Ohka, “Object Handling Using Artificial Tactile Affordance System”, International Journal of Computer and Network Security, Vol. 2, No. 10, pp. 32-37, 2010.
[8] P. H. Winston, “Artificial Intelligence (second edition),” Addison-Wesley, pp. 159-204, 1984.
[9] J. Haugeland, “Artificial Intelligence: The Very Idea”, MIT Press, 1985.
[10] S. Harnad, “The Symbol Grounding Problem”, Physica, D 42: 335-346, 1990.
[11] J. McCarthy and P. J. Hayes, “Some philosophical problems from the standpoint of artificial intelligence”, Machine Intelligence, 4:463-502, 1969.
[12] J. McCarthy, “Applications of circumscription to formalizing common-sense knowledge. Artificial Intelligence”, 28:89-116, 1986.
[13] D. Levy & M. Newborn, “How to computer play chess”, W. H. Freeman and Company, 1991.
[14] V. Braitenberg, “Adaptation in Natural and Artificial Systems,” MIT Press, 1984
[15] R. A. Brooks, “A robust layered control system for a mobile robot,” IEEE Journal of Robotics and Automation, RA-2(1), pp. 14-23, 1986.
[16] R. A. Brooks, “Intelligence without representation,” Technical Report MIT AI Lab, 1988.
[17] R. A. Brooks, “A Robot That Walks; Emergent Behaviors from a Carefully Evolved Network”, MIT AI Lab Memo 1091, 1989.
[18] J. J. Gibson, “The Ecological Approach to Visual Perception,” Houghton Mifflin Company, 1979.
[19] M. Niitsuma, W. Beppu, T. Ichikawa, S. Kovács, P. Korondi, “Implementation of Robot Behaviors Based on Ethological Approach for Monitoring Support System in Intelligent Space”, H., 2011 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM2011), pp. 536-541, 2011.
[20] D. E. Goldberg, “Genetic Algorithms in Search, Optimization and Machine Learning,” Addison Wesley, 1989.
[21] T. Fukuda, T. Ueyama, and F. Arai, “Control strategy for a network of cellular robots,” IEEE International Conference on Robotics and Automation, pp. 1616-1621, 1991.
[22] R. C. Arkin, “Cooperation without communication: Multi-agent schema-based robot navigation,” Journal of Robotic Systems, 9(3), pp. 351-364, 1992.
[23] M. K. Habib, H. Asama, Y. Ishida, A. Matsumoto, and I. Endo, “Simulation environment for an autonomous and decentralized multi-agent robotic system,” IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 1550-1557, 1992.
[24] C. R. Kube and H. Zhang, “Controlling Collective Tasks With An ALN,” IEEE/RSJ International Conference on Intelligent Robots and Systems, pp. 289-293, 1993.
[25] T. Hoshino, D. Mitsumoto, and T. Nagano, “Evolution of Robot Behavior and Its Robustness,” Transactions of the Society of Instrument and Control Engineers (Transactions of SICE), Vol. 33, No 6, 533-540, 1997.
[26] H. B. Yussof, J. Wada, and M. Ohka, Object Handling Tasks Based on Active Tactile and Slippage Sensations in Multi-Fingered Humanoid Robot Arm, 2009 IEEE International Conference on Robotics and Automation, pp. 502-507, 2009.
[27] M. Ohka, H. Kobayashi, J. Takata, and Y. Mitsuya, An Experimental Optical Three-axis Tactile Sensor Featured with Hemispherical Surface, Journal of Advanced Mechanical Design, Systems, and Manufacturing, Vol. 2, No. 5, pp. 860-873, 2008.
[28] M. Ohka, J. Takata, H. Kobayashi, H. Suzuki, N. Morisawa, and H. B. Yussof, Object Exploration and Manipulation Using a Robotic Finger Equipped with an Optical Three-axis Tactile Sensor, Robotica, Vol. 27, pp. 763-770, 2009.