Fuzzy Inference Model for Learning from Experiences and Its Application to Robot Navigation

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.

• Type: Conference
• Year: 2005
• Where: CIMCA
• Authors: Manabu Gouko, Yoshihiro Sugaya, Hirotomo Aso
• Keywords: CIMCA 2005, Experiences, Fuzzy Inference Model, Model