Applications of Classifying Bidding Strategies for the CAT Tournament
- 325 Downloads
In the CAT Tournament, specialists facilitate transactions between buyers and sellers with the intention of maximizing profit from commission and other fees. Each specialist must find a well-balanced strategy that allows it to entice buyers and sellers to trade in its market while also retaining the buyers and sellers that are currently subscribed to it. Classification techniques can be used to determine the distribution of bidding strategies used by all traders subscribed to a particular specialist. Our experiments showed that Hidden Markov Model classification yielded the best results. The distribution of strategies, along with other competition-related factors, can be used to determine the optimal action in any given game state. Experimental data shows that the GD and ZIP bidding strategies are more volatile than the RE and ZIC strategies. An MDP framework for determining optimal actions given an accurate distribution of bidding strategies is proposed as a motivator for future work.
KeywordsHide Markov Model Markov Decision Process Conditional Random Field Bidding Strategy Total Trader
Unable to display preview. Download preview PDF.
- 1.Gerding, E., McBurney, P., Niu, J., Parsons, S., Phelps, S.: Overview of CAT: A Market Design Competition. Technical Report ULCS-07-006. Department of Computer Science, University of Liverpool. Liverpool, UK (2007), http://www.csc.liv.ac.uk/research/techreports/tr2007/tr07006abs.html
- 2.Cliff, D.: Minimal-intelligence agents for bargaining behaviors in market-based environments. Technical Report HP–97–91, Hewlett Packard Laboratories, Bristol, England (1997)Google Scholar
- 6.Niu, J., Cai, K., Parsons, S., Gerding, E., McBurney, P., Moyaux, T., Phelps, S., Shield, D.: JCAT: A platform for the TAC Market Design Competition. In: Proceedings of the Seventh International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS 2008), Estoril, Portugal (May 2008)Google Scholar
- 8.Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines (2001), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
- 9.Bilmes, J.A.: What HMMs can do. IEICE Transactions on Information and Systems E89-D(3), 869–891 (2006)Google Scholar
- 10.Rabiner, L.R.: A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. In: Proceedings of the IEEE International Conference, vol. 77(2), pp. 257–286 (February 1989)Google Scholar
- 11.Ruping, S.: SVM kernels for time series analysis (Technical Report), Department of Computer Science. University of Dortmund, Dortmund, Germany (2001)Google Scholar
- 12.Grauman, K., Darrell, T.: The Pyramid Match kernel: discriminative classification with sets of image features. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Beijing, China (October 2005)Google Scholar
- 13.Russell, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall Series in Artificial Intelligence, Englewood Cliffs, New Jersey (1995)Google Scholar