A fuzzy reinforcement function for the intelligent agent to process vague goals

The intelligent agent is one of the most interesting fields of Artificial Intelligence study. Generally, very many kinds of the intelligent agent receive the user´s goal and they try to solve it with their expert knowledge. The user´s goals or requests can be represented with the human language, and they contain the uncertainties of the human knowledge. While the intelligent agent must represent these vague goals and understand the user´s desires or intentions, there have not been enough researches done for the intelligent agents to express the user´s goals. In this paper, we propose a new method to represent the vague goals as well as the uncertain environments. We suggest a fuzzy goal and a fuzzy state representation. We extend the traditional reinforcement learning to the fuzzy reinforcement learning with defining the fuzzy reinforcement function by using the fuzzy goal and the fuzzy stare. We, also propose a new Fuzzy Q-Learning algorithm. The experiment results show the better performance of the learning, and the reasonability of the fuzzy reinforcement learning