Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30184
Enhancement Approaches for Supporting Default Hierarchies Formation for Robot Behaviors

Authors: Saeed Mohammed Baneamoon, Rosalina Abdul Salam

Abstract:

Robotic system is an important area in artificial intelligence that aims at developing the performance techniques of the robot and making it more efficient and more effective in choosing its correct behavior. In this paper the distributed learning classifier system is used for designing a simulated control system for robot to perform complex behaviors. A set of enhanced approaches that support default hierarchies formation is suggested and compared with each other in order to make the simulated robot more effective in mapping the input to the correct output behavior.

Keywords: Learning Classifier System, Default Hierarchies, Robot Behaviors.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1079242

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1070

References:


[1] D. E. Goldberg, " Genetic Algorithms in Search, Optimization ", and Machine Learning, Addison-Wesley, Reading, Mass, 1989.
[2] M. Dorigo and M. Colombetti, " Robot shaping: An experiment in behavior engineering ", The MIT Press, Cambridge, Massachusetts, London, England, 1998.
[3] P-L Lanzi, W. Stolzmann, and S. W. Wilson, " Learning Classifier Systems: From Foundations to Applications", Springer, 2000.
[4] S. M. Baneamoon, R. Abdul Salam, A. Z. Hj. Talib, " Learning Process Enhancement for Robot Behaviors", International Journal of Intelligent Technology, Volume 2 Number 3, ISSN 1305-6417, 2007, pp. 172-177.
[5] D. E. Goldberg. " Sizing populations for serial and parallel genetic algorithms " - Third international conference on genetic algorithms , George Mason University, United States 1989, pp. 70 - 79
[6] M. Dorigo " New Perspective about Default Hierarchies Formation in Learning Classifier System", Proceedings of the 2nd Congress of the Italian Association for Artificial Intelligence on Trends in Artificial Intelligence, Lecture Notes in Artificial Intelligence, Vol. 549, Springer- Verlag London, UK 1991, pp. 218 - 227.
[7] M. Dorigo, " Message-Based Bucket Brigade: An Algorithm for the Apportionment of Credit Problem ", Lecture Notes in Artificial Intelligence 482, Springer, Berlin , 1991, pp. 235-244.
[8] M. Dorigo, " Genetic-Based Machine Learning and Behavior-Based Robotics: A New Synthesis ". 0018-9472/93, IEEE TRANSACTIONS ON SYSTEM. MAN. And CYBERNETICS, Vol. 23, No.1, January/ February 1993, pp. 141 - 154.
[9] M. Studely & L. Bull, " X-TCS: Accuracy-based Learning Classifier System Robotics ", In Proceedings of the IEEE Congress on Evolutionary Computation, Volume 3, 2005, pp. 2099 - 2106..
[10] P. Musilek, Sa Li, and L. Wyard-Scot, " Enhanced Learning Classifier System for Robot Navigation ", IROS 2005, IEEE/RSJ International Conference on Intelligent Robots and Systems, Alberta, Canada, 2-6 Aug. 2005, pp 3390- 3395.
[11] R. E. Smith and D. E. Goldberg. "Reinforcement Learning with Classifier System " - AI, Simulation, and Planning in High Autonomy System ., Proceedings. , IEEE, 1990, pp. 184 - 192.
[12] S. J. Bay, " Learning Classifier Systems for Single and Multiple Mobile Robots in Unstructured Environments", Mobile Robots X. Philadelphia, PA, Nov. 1995, pp. 88-99.
[13] S. M. Baneamoon, R. Abdul Salam, " Bucket Brigade Algorithm Enhancement for Robot Behaviors ", International Conference on Robotics, Vision, Information and Signal Processing (ROVISP 2007), Penang, Malaysia, 28-30 November 2007, pp. 930-934.
[14] A. C. Schultz, " Learning robot behaviors using genetic algorithms ", Proceedings of the First World Automation Congress (WAC -94), TSIP Press: Albuquerque, New Mexico, August 1994, pp. 607-612.