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.
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References
Abul, O., Polat, F., Alhajj, R.: Multiagent reinforcement learning using function approximation. IEEE TSMC 30(4), 485–497 (2000)
Baird, L.: Residual algorithms: Reinforcement learning with function approximation. In: Proc. of ICML, pp. 30–37 (1995)
Chaudhuri, S., Dayal, U.: An overview of data warehousing and OLAP technology. ACM SIGMOD Record 26, 65–74 (1997)
Han, J., Fu, Y.: Mining multiple-level association rules in large databases. IEEE TKDE 11(5), 798–804 (1999)
Hu, J., Wellman, M.P.: Multiagent reinforcement learning: theoretical framework and an algorithm. In: Proc. of ICML, pp. 242–250 (1998)
Kaya, M., Alhajj, R.: Reinforcement Learning in Multiagent Systems: A Modular Fuzzy Approach with Internal Model Capabilities. In: Proc. of IEEE ICTAI, pp. 469–475 (2002)
Kuok, C.M., Fu, A.W., Wong, M.H.: Mining fuzzy association rules in databases. SIGMOD Record 17(1), 41–46 (1998)
Littman, M.L.: Markov games as a framework for multi agent reinforcement learning. In: Proc. of ICML, pp. 157–163 (1994)
Nagayuki, Y., Ishii, S., Doya, K.: Multi-Agent reinforcement learning: An approach based on the other agent’s internal model. In: Proc. of IEEE ICMAS, pp. 215–221 (2000)
Tan, M.: Multi-agent reinforcement learning: independent vs cooperative agents. In: Proc. of ICML, pp. 330–337 (1993)
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© 2004 Springer-Verlag Berlin Heidelberg
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Kaya, M., Alhajj, R. (2004). Fuzzy OLAP Association Rules Mining Based Novel Approach for Multiagent Cooperative Learning. In: Orchard, B., Yang, C., Ali, M. (eds) Innovations in Applied Artificial Intelligence. IEA/AIE 2004. Lecture Notes in Computer Science(), vol 3029. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24677-0_7
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DOI: https://doi.org/10.1007/978-3-540-24677-0_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22007-7
Online ISBN: 978-3-540-24677-0
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