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.

Volume

1

fYear

2005

fDate

28-30 Nov. 2005

Firstpage

577

Lastpage

582

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/CIMCA.2005.1631325

Filename

1631325