By Topic

Multiagent reinforcement learning using OLAP-based association rules mining

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. Eng., Firat Univ., Elazig, Turkey ; Alhajj, R.

In this paper, we propose a novel multiagent learning approach, which is based on online analytical processing (OLAP) data mining. First, we describe a data cube OLAP architecture which facilitates effective storage and processing of the state information reported by agents. This way, the action of the other agent, even not in the visual environment of the agent under consideration, can simply be estimated by extracting online association rules from the constructed data cube. Then, we present a new action selection model which is also based on association rules mining. Finally, we generalize states which are not experienced sufficiently by mining multiple-levels association rules from the proposed data cube. Experiments conducted on a well-known pursuit domain show the effectiveness of the proposed learning approach.

Published in:

Intelligent Agent Technology, 2003. IAT 2003. IEEE/WIC International Conference on

Date of Conference:

13-16 Oct. 2003