Title :
Fuzzy Inference Model for Learning from Experiences and Its Application to Robot Navigation
Author :
Gouko, Manabu ; Sugaya, Yoshihiro ; Aso, Hirotomo
Author_Institution :
Dept. of Electr. & Commun. Eng., Tohoku Univ.
Abstract :
A fuzzy inference model for learning from experiences (FILE) is proposed. the model can learn from experience data obtained by trial-and-error of a task and it can stably learn from both experiences of success and failure of a trial. the learning of the model is executed after each of trial of the task. hence, it is expected that the achievement rate increases with repetition of the trials, and that the model adapts to change of environment. in this paper, we confirm performance of the model by applying the model to a robot navigation task simulation and investigate the knowledge acquired by the learning
Keywords :
fuzzy reasoning; learning (artificial intelligence); path planning; robots; FILE model; fuzzy inference model; learning from experience; robot navigation task simulation; Computational modeling; Genetics; Humans; Intelligent robots; Intelligent systems; Learning systems; Navigation; Robotics and automation; Supervised learning; Training data;
Conference_Titel :
Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on
Conference_Location :
Vienna
Print_ISBN :
0-7695-2504-0
DOI :
10.1109/CIMCA.2005.1631325