By Topic

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

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Ho-Sub Seo ; Inf. Commun. R&D Center, DACOM, Seoul, South Korea ; So-Joeng Youn ; Kgung-Whan Oh

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

Published in:

Fuzzy Information Processing Society, 2000. NAFIPS. 19th International Conference of the North American

Date of Conference:

2000