Utilizing fuzzy OLAP mining towards novel approach to multiagent modular reinforcement learning

This study proposes novel multiagent learning approach based on utilizing fuzzy mining for modular cooperative learning systems. It incorporates fuzziness and online analytical processing (OLAP) based mining to effectively process the information reported by agents. First, we describe a fuzzy data cube OLAP architecture to facilitate 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 predicted by extracting online association rules from the constructed data cube. Second, we present a new action selection model, also based on association rules mining. Finally, we generalize not sufficiently experienced states, by mining multi-level association rules from the proposed fuzzy data cube. Experimental results obtained on a well-known pursuit domain show the robustness and effectiveness of the proposed approach.