Abstract—An efficient retrieval of a relatively small number
of relevant cases from a huge ease base is a crucial subtask of
Case-Based Reasoning. Moreover, Motion Controlling for
Humanoid Robot is a very complex problem. In this paper, we
propose the application of case-retrieval nets techniques in the
design of our previously proposed motion controller model for
humanoid robots. It depends on case-based reasoning (CBR)
methodology. Our main goal is to enhance the retrieval
accuracy of the case-based controller of the humanoid soccer.
The controller is being implemented in the framework of
Webots Simulation Tool for the NAO Humanoid Robot. The
main motivation of this paper is to improve the retrieval
accuracy of our HCBR behavior controller, develop an
automatic real-time CBR-Retrieval Algorithm for robot, and
improve the storage capacity of the case-memory. We also
describe the implementation of our extended retrieval CBR
algorithm that shows good results for controlling the NAO.
Future research directions and ideas for developing each
module are also discussed.
Index Terms—Humanoid robot, RoboCup, artificial intelligence, case-based reasoning, webots, motion controller.
M. Altaf. is with Center of Robotics and Intelligent Systems at King Abdel-Aziz City for Science and Technology, KACST, Riyad, Kingdom of Saudi Arabia (e-mail: email@example.com).
B. Elbagoury was with Humboldt German Team of Robotics, NAO Humanoid Team, Berlin, Germany and Robotics and Computer Science at Faculty of Computers and Information Sciences, Ain Shams University, Cairo, Egypt (e-mail: firstname.lastname@example.org).
S. Ghoniemy is with Faculty of Computers and Information Science, Ain Sham Uiversity, Cairo, Egypt. He is also with the College of Computers and Information Technology, Taif university, Taif, KSA (e-mail: email@example.com).
Cite: Meteb M. Altaf, Bassant M. El Bagoury, Fahad Alraddady, and Said Ghoniemy, "Enhancing Case-Based Retrieval Engine with Case Retrieval Nets for Humanoid Robot Motion Controller," International Journal of Machine Learning and Computing vol. 5, no. 3, pp. 235-241, 2015.