Enhancement Approaches for Supporting Default Hierarchies Formation for Robot Behaviors
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:61486", author = "Saeed Mohammed Baneamoon and Rosalina Abdul Salam", title = "Enhancement Approaches for Supporting Default Hierarchies Formation for Robot Behaviors", 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.", volume = "3", number = "2", pages = "452-5", }
