Fuzzy inference model for learning from experiences and its application to robot navigation

Manabu Gouko, Yoshihiro Sugaya, Hirotomo Aso

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A fiizzy 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.

Original languageEnglish
Title of host publicationProceedings - International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet
Pages577-582
Number of pages6
Volume1
Publication statusPublished - 2005 Dec 1
EventInternational Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, IAWTIC 2005 - Vienna, Austria
Duration: 2005 Nov 282005 Nov 30

Other

OtherInternational Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, IAWTIC 2005
CountryAustria
CityVienna
Period05/11/2805/11/30

ASJC Scopus subject areas

  • Engineering(all)

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