Fuzzy OLAP association rules mining based novel approach for multiagent cooperative learning

Abstract

In this paper, we propose a novel multiagent learning approach for cooperative learning systems. Our approach incorporates fuzziness and online analytical processing (OLAP) based data mining to effectively process the information reported by the agents. 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 fuzzy data cube. Results obtained for a well-known pursuit domain show the robustness and effectiveness of the proposed fuzzy OLAP mining based learning approach.