Abstract
In agent-based markets, adapting to the behavior of other agents is often necessary for success. When it is not possible to directly model individual competitors, an agent may instead model and adapt to the market conditions that result from competitor behavior. Such an agent could still benefit from reasoning about specific competitor strategies by considering how various combinations of these strategies would impact the conditions being modeled. We present an application of such an approach to a specific prediction problem faced by the agent TacTex-06 in the Trading Agent Competition’s Supply Chain Management scenario (TAC SCM).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Carmel, D., Markovitch, S.: Opponent modeling in multi–agent systems. In: Weiss, G., Sen, S. (eds.) IJCAI-WS 1995. LNCS, vol. 1042, pp. 40–52. Springer, Heidelberg (1996)
Hu, J., Wellman, M.P.: Online learning about other agents in a dynamic multiagent system. In: Sycara, K.P., Wooldridge, M. (eds.) Proceedings of the 2nd International Conference on Autonomous Agents, pp. 239–246. ACM Press, New York (1998)
Sadeh, N., Hildum, D., Kjenstad, D., Tseng, A.: Mascot: an agent-based architecture for coordinated mixed-initiative supply chain planning and scheduling. In: Workshop on Agent-Based Decision Support in Managing the Internet-Enabled Supply-Chain, at Agents 1999 (1999)
Chen, Y., Peng, Y., Finin, T., Labrou, Y., Cost, S.: A negotiation-based multi-agent system for supply chain management. In: Workshop on Agent-Based Decision Support in Managing the Internet-Enabled Supply-Chain, at Agents 1999 (1999)
Collins, J., Arunachalam, R., Sadeh, N., Eriksson, J., Finne, N., Janson, S.: The supply chain management game for the 2006 trading agent competition. Technical report (2005), http://www.sics.se/tac/tac06scmspec_v16.pdf
Pardoe, D., Stone, P.: TacTex-2005: A champion supply chain management agent. In: Proceedings of the Twenty-First National Conference on Artificial Intelligence, pp. 1489–1494 (2006)
Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco (1999)
Stone, P., Schapire, R.E., Littman, M.L., Csirik, J.A., McAllester, D.: Decision-theoretic bidding based on learned density models in simultaneous, interacting auctions. Journal of Artificial Intelligence Research 19, 209–242 (2003)
Kiekintveld, C., Wellman, M., Singh, S., Estelle, J., Vorobeychik, Y., Soni, V., Rudary, M.: Distributed feedback control for decision making on supply chains. In: Fourteenth International Conference on Automated Planning and Scheduling (2004)
He, M., Rogers, A., Luo, X., Jennings, N.R.: Designing a successful trading agent for supply chain management. In: AAMAS 2006: Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 1159–1166. ACM, New York (2006)
Littlestone, N., Warmuth, M.: The weighted majority algorithm. Information and Computation 108, 212–261 (1994)
Rettinger, A., Zinkevich, M., Bowling, M.: Boosting expert ensembles for rapid concept recall. In: Proceedings of the Twenty-First National Conference on Artificial Intelligence (2006)
Thrun, S., Pratt, L. (eds.): Learning To Learn. Kluwer Academic Publishers, Dordrecht (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Pardoe, D., Stone, P. (2008). Adapting Price Predictions in TAC SCM. In: Collins, J., et al. Agent-Mediated Electronic Commerce and Trading Agent Design and Analysis. AMEC TADA 2007 2007. Lecture Notes in Business Information Processing, vol 13. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88713-3_3
Download citation
DOI: https://doi.org/10.1007/978-3-540-88713-3_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88712-6
Online ISBN: 978-3-540-88713-3
eBook Packages: Computer ScienceComputer Science (R0)