Using learning automata for tuning fuzzy membership functions in learning driver preferences

With the growth of car navigation systems technology come a variety of enhancements aiming to increase user comfort and satisfaction. One such application is the appearance of methods for learning a driverpsilas preferences in making a choice between several routes. A driver may know his/her basic and most important factors in making such a decision, but may have these factors weighing in differently. Hence, machine learning methods can be applied to model the driverpsilas preferences, thus predicting the result of the decision process. This paper proposes a new method which combines a fuzzy expert system approach with learning automata.