Learning fuzzy rules by evolution for mobile agent control

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