학술논문
Learning fuzzy rules by evolution for mobile agent control
Document Type
Conference
Author
Source
Proceedings 1999 IEEE International Symposium on Computational Intelligence in Robotics and Automation. CIRA'99 (Cat. No.99EX375) Computational intelligence in robotics and automation Computational Intelligence in Robotics and Automation, 1999. CIRA '99. Proceedings. 1999 IEEE International Symposium on. :70-76 1999
Subject
Language
Abstract
We propose a learning mechanism for mobile agent navigation. The agent is controlled by a dynamic set of fuzzy rules, where the rule set is learned using genetic algorithms. The rules are adjusted during a training session and tested after satisfactory behavior is observed. This approach provides for learning different navigation schemes, depending on the required behavior of the agent, without dramatic changes in the code, except for the evaluation function. In this work we tested the learning scheme for a situation where the agent has to approach a given set of 2D coordinates, while avoiding obstacles in an unknown dynamic environment.