Change Detection-Based Method to Determine Customer Order Offer Price in the TAC-SCM Environment
Abstract
This paper presents an adaptation of an approach based on Drift Detection and instance based machine-learning. This adapted approached is applied to agents in stochastic simulation scenarios of the TAC-SCM (Trading Agent Competition - Supply Chain Management) environment in order to determine selling prices of goods. In short, this approach allows detecting frequent market changes and determining a competitive price based on feedback obtained from negotiations with clients. The proposed approach is evaluated by practical experiments. It is also concomitantly presented an approach to minimize the expensive computable cost caused by the instance based machine learning technique used in the experiments.
Keywords:
Aprendizagem Baseada em Instâncias, Dynamic Weighted Majority, Previsão de Preços
References
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Collins, J. Arunachalam, R. Sadeh, N. Eriksson, J. Finne, N. Janson, S. The Supply Chain Management Game For The 2007 Trading Agent Competition. Pittsburgh, School Of Computer Science Carnegie Mellon University, 2007.
Cover, T.M., Hart, P.E.. Nearest Neighbor Pattern Classification. Institute Of Electrical And Electronics Engineers Transactions On Information Theory, 13, 21-27, 1967.
COYLE J.J., BARDI, E.J., LANGLEY, C.J., Supply Chain Management: A Logistics Perspective. South-Western College Pub; 8 Edition, 2008.
Deza, E., Deza, M., Dictionary Of Distances. Elsevier, ISBN 0444520872, 2006.
Enembreck, F. Tacla, C. A. Barthès, J. P. Learning Negotiation Policies Using Ensemble-Based Drift Detection Techniques. International Journal On Artificial Intelligence Tools - World Scientic Publishing Company, 2009.
Gama, J., Medas, P., Castillo, G., Rodrigues, P. Learning With Drift Detection. Proc. Of The 17th Brazilian Symposium On Artificial Intelligence – Sbia’ 04, Lnai 3171, Ana L.C. Bazzan And Sofiane Labidi (Eds.), Springer, Pp.286-295, Brazil. Isbn 3-540-23237-0, 2004.
Helmbold, D. P., Long, P. M. Tracking Drifting Concepts By Minimizing Disagreements. Machine Learning, 14(1):27-46, 1994.
Knuth, D. The Art Of Computer Programming, Volume 3: Sorting And Searching, Third Edition. Addison-Wesley. ISBN 0-201-89685-0. Section 6.2.1: Searching An Ordered Table, Pp. 409–426, 1997.
Kolter, J. Z; Maloof, M. A. Dynamic Weighted Majority: A New Ensemble Method For Tracking Concept Drift. Ieee Conference On Data Mining, 2003.
Mitchell, T. Machine Learning. New York: Mcgraw Hill, 1997.
Russell, S. Norvig, P. Inteligência Artificial. Elsevier Editoria Ltda, 2004.
Shoham, Y., Leyton-Brown, K.. Multiagent Systems: Algorithmic, Game-Theoretic, And Logical Foundations. Cambridge University Press, 2009.
Simchi-Levi, D., Kaminsky, P., Simchi-Levi, E. Designing And Managing The Supply Chain. Mcgraw-Hill Higher Education, 2000.
Tranvouez, E. Ferrarini, A. Multiagent Modelling Of Cooperative Disruption Management In Supply Chains. IEEE, 2006.
Published
2010-04-19
How to Cite
PEREIRA, Fernando Roberto; BANASZEWSKI, Roni Fabio; SIMÃO, Jean Marcelo; TACLA, Cesar Augusto.
Change Detection-Based Method to Determine Customer Order Offer Price in the TAC-SCM Environment. In: WORKSHOP-SCHOOL ON AGENTS, ENVIRONMENTS, AND APPLICATIONS (WESAAC), 4. , 2010, Rio Grande/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2010
.
p. 41-52.
ISSN 2326-5434.
DOI: https://doi.org/10.5753/wesaac.2010.33052.
