Acquisition of Knowledge for Gymnastic Bar Action by Active Learning Method
Yoshitaka Sakurai, Nakaji Honda, and Junji Nishino
Department of systems Engineering, University of Electro-Communications, 1-5-1, Chofugaoka, Chofu-shi, Tokyo, 182-8585 Japan
Received:August 28, 2002Accepted:December 13, 2002Published:February 20, 2003
Keywords:fuzzy-modeling, active learning, gymnastic bar model, link-model, reinforcement learning
In this paper, we aim at engineering realization of human active learning function by top-down approach noticing the macro functions of the brain. Concretely, we propose the Active Learning Method, the method to acquire the control knowledge actively by the method of trial and error. In this method, the input-output information is collected for the control object by the method of trial and error, and the controller is constructed based on the information. The active learning is the learning form in which the information is acquired from the behavior which the learner himself takes. In the Active Learning Method, the output is decided actively and the action result is evaluated, and the data with high evaluation are modeled. This modeled pattern information becomes the behavior policy optimized based on the evaluation. For this modeling, the method called Ink Drop Spread method (IDS) is used. In this system, the object system is modeled functionally from the data by the fuzzy-like processing. It is not the linguistic approach like fuzzy inference but represents the knowledge by the pattern-like approach. By using the model of bar gymnast, the learning simulation is done for the behavior policy, and we examine the validity of this method.
Cite this article as:Y. Sakurai, N. Honda, and J. Nishino, “Acquisition of Knowledge for Gymnastic Bar Action by Active Learning Method,” J. Adv. Comput. Intell. Intell. Inform., Vol.7 No.1, pp. 10-18, 2003.Data files: