Technical Note: \cal Q-Learning
Machine Learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
A New Approach to Online Generation of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
ICTAI '02 Proceedings of the 14th IEEE International Conference on Tools with Artificial Intelligence
Multi-Agent Reinforcement Learning: An Approach Based on the Other Agent's Internal Model
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Multiagent reinforcement learning using function approximation
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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In this paper we propose a novel learning approach which integrates online analytical processing (OLAP) based data mining into the learning process. 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 enviaronment of the agent and consideration, can simply be estimated by extracting online association rules, a well-known data mining technique, 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 robustness and effectiveness of the proposed learning approach.