By Topic

Reinforcement learning in multiagent systems: a modular fuzzy approach with internal model capabilities

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

2 Author(s)
Kaya, M. ; Dept. of Comput. Sci., Calgary Univ., Alta., Canada ; Alhajj, R.

Most of the methods proposed to improve the learning ability in multiagent systems are not appropriate to more complex multiagent learning problems because the state space of each learning agent grows exponentially in terms of the number of partners present in the environment. We propose a novel and robust multiagent architecture to handle these problems. The architecture is based on a learning fuzzy controller whose rule base is partitioned into several different modules. Each module deals with a particular agent in the environment and the fuzzy controller maps the input fuzzy sets to the output fuzzy sets that represent the state space of each learning module and the action space, respectively. Also, each module uses an internal model table to estimate the action of the other agents. Experimental results show the robustness and effectiveness of the proposed approach.

Published in:

Tools with Artificial Intelligence, 2002. (ICTAI 2002). Proceedings. 14th IEEE International Conference on

Date of Conference: