Information Technology Reference
In-Depth Information
We have demonstrated the effectiveness of our approach by characterizing the market
conditions in games played in the semi-finals and finals from TAC SCM 2004 and 2005.
Our next step is to complete the prediction of future regimes, to design and evaluate
sales strategies that take advantage of regime prediction, and to integrate them into the
decision making process of our agent. We believe that our proposed formulation will
allow the agent to operate effectively on a daily basis as well as to engage in strategic
pricing.
Acknowledgements
Partial funding for this work is acknowledged from NSF under grant IIS-0414466.
References
1. Sadeh, N., Arunachalam, R., Eriksson, J., Finne, N., Janson, S.: TAC-03: A supply-chain
trading competition. AI Magazine 24(1) (2003) 9294
2. Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via
the EM algorithm. Journal of the Royal Statistical Society Series B, 39(1) (1977) 138
3. Levinson, S.E.: Continuously variable duration hidden markov models for automatic speech
recognition. Comput. Speech Lang. 1(1) (1986) 2945
4. Pauwels, K., Hanssens, D.: Windows of Change inMatureMarkets. In: EuropeanMarketing
Academy Conf., Braga, Portugal (2002)
5. Cherkassky, V.,Mulier, F.: Learning from data - Concepts, Theory, andMethods. JohnWiley
& Sons, INC., New York (1998)
6. Ng, A., Russell, S.: Algorithms for inverse reinforcement learning. In: Proc. of the 17th Intl
Conf. on Machine Learning, Palo Alto (2000) 663670
7. Carmel, D., Markovitch, S.: Learning models of opponents strategy in game playing. Tech-
nical report, Technion-Israel Institute of Technology (1993)
8. Chajewska, U., Koller, D., Ormoneit, D.: Learning an agents utility function by observing
behavior. In: Proc. of the 18th Intl Conf. on Machine Learning, Lafayette (2001) 3542
9. von Neumann, J., Morgenstern, O.: Theory of Games and Economic Behavior. Princeton
University Press, 2nd edition, Princeton, N.J. (1947)
10. Pardoe, D., Stone, P.: Bidding for Customer Orders in TAC SCM: A Learning Approach. In:
Workshop on Trading Agent Design and Analysis at AAMAS, New York (2004) 5258
11. Benisch, M., Greenwald, A., Grypari, I., Lederman, R., Naroditskiy, V., Tschantz, M.: Botti-
celli: A supply chain management agent designed to optimize under uncertainty. ACM Trans.
on Computational Logic 4(3) (2004) 2937
12. Ketter,W., Kryzhnyaya, E., Damer, S., McMillen, C., Agovic, A., Collins, J., Gini,M.: Min-
neTAC sales strategies for supply chain TAC. In: Proc. of the Third Intl Conf. on Autonomous
Agents and Multi-Agent Systems, New York (2004) 13721373
13. Ketter,W., Kryzhnyaya, E., Damer, S.,McMillen, C., Agovic, A., Collins, J., Gini,M.: Analy-
sis and design of supply-driven strategies in TAC-SCM. In: Workshop: Trading Agent Design
and Analysis at the Third Intl Conf. on Autonomous Agents and Multi-Agent Systems, New
York (2004) 4451
14. Dahlgren, E., Wurman, P.: PackaTAC: A conservative trading agent. SIGecom Exchanges
4(3) (2004) 3340
15. Wellman, M.P., Estelle, J., Singh, S., Vorobeychik, Y., Kiekintveld, C., Soni, V.: Strategic
interactions in a supply chain game. Computational Intelligence 21(1) (2005) 126
Search WWH ::




Custom Search