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 language | English |
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| Title of host publication | Proceedings - International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet |
| Pages | 577-582 |
| Number of pages | 6 |
| Volume | 1 |
| Publication status | Published - 2005 Dec 1 |
| Event | International 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 28 → 2005 Nov 30 |
Other
| Other | International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, IAWTIC 2005 |
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| Country/Territory | Austria |
| City | Vienna |
| Period | 05/11/28 → 05/11/30 |
ASJC Scopus subject areas
- Engineering(all)