Predicting people's bidding behavior in negotiation

Proceedings of the International Conference on Autonomous Agents

Volumn 2006, Issue , 2006, Pages 370-376

  Gal, Ya'akov ^a   Pfeffer, Avi ^a  

^a HARVARD UNIVERSITY   (United States)

Author keywords

Decision making; Negotiation; Opponent modeling

Indexed keywords

BIDDING BEHAVIOR; DATA COLLECTION; NEGOTIATION; OPPONENT MODELING; SYNTHETIC DATA SETS;

ALGORITHMS; APPROXIMATION THEORY; DATA ACQUISITION; DATA STRUCTURES; DECISION MAKING; PROBLEM SOLVING; STATISTICAL METHODS;

LEARNING SYSTEMS;

EID: 34247190723     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1160633.1160699     Document Type: Conference Paper

References (21)

1. R. Arunachalam, J. Eriksson, N. Finne, S. Janson, and N. Sadeh. The TAC supply chain management game. Technical report, Swedish Institute of Computer Science, 2003.
2. M. Bazerman. Judgment in Managerial Decision Making. Wiley, 2001.
3. C. Camerer. Behavioral Game Theory: Experiments in Strategic Interaction. Princeton University Press, 2003.
4. J. Cavano. Computers as partners: A technology forecast for decision-making in the 21st century. In Proceedings of the Twenty-Third Annual International Computer Software and Applications Conference, 2001.
5. U. Chajewska and D. Koller. Utilities as random variables. In Proc. 16th Conference on Uncertainty in Artificial Intelligence (UAI), 2001.
6. U. Chajewska, D. Koller, and D. Ormoneit. Learning an agent's utility function by observing behavior. In Proc. 17th International Joint Conference on Artificial Intelligence (IJCAI), 2001.
7. R. Das, J. E. Hanson, J. O. Kephart, and G. Tesauro. Agent-human interactions in the continuous double auction. In IJCAI, pages 1169-1187, 2001.
8. A. Dempster, N. Laird, and D. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 39(1), 1977.
9. N. Friedman, L. Getoor, D. Koller, and A. Pfeffer. Learning probabilistic relational models. In Proc. 16th International Joint Conference on Artificial Intelligence (IJCAI), 1999.
10. Y. Gal, A. Pfeffer, F. Marzo, and B. Grosz. Learning social preferences in games. In Proc. 19th National Conference on Artificial Intelligence (AAAI), 2004.
11. B. Grosz, S. Kraus, S. Talman, and B. Stossel. The influence of social dependencies on decision-making. Initial investigations with a new game. In Proc. 3rd International Joint Conference on Multi-agent systems (AAMAS), 2004.
12. J. Henrich, R. Boyd, S. Bowles, H. Gintis, E. Fehr, and C. Camerer, editors. Foundations of Human Sociality: Economic Experiments and Ethnographic Evidence in Fifteen Small-Scale Societies. Oxford University Press, 2004.
13. J. Kagel and A. Roth, editors. The hanbook of experimental economics. Princeton University Press, 1995.
14. R. Lawrence. A machine-learning approach to optimal bid pricing. In Proceedings of the Eighth INFORMS Computing Society Conference on Optimization and Computation in the Network Era, Arizona, 2003. 6, 2003.
15. G. Loewenstein, M. Bazerman, and L. Thompson. Social utility and decision making in interpersonal contexts. Journal of Personality and Social psychology, (57)(3):426-441, 1989.
16. J. MacKie-Mason, A. Osepayshivili, D. Reeves, and M. Wellman. Price prediction strategies for market-based scheduling. In Proc. of 18th International Conference on Automated Planning and Scheduling, June 2004.
17. R. Neal. Connectionist learning of belief networks. Artificial Intelligence, 56:71-113, 1992.
18. A. Ng and S. Russell. Algorithms for inverse reinforcement learning. In Proc. 17th International Joint Conference on Artificial Intelligence (IJCAI), 2000.
19. D. Pardoe and P. Stone. Bidding for customer orders in TAC SCM. In P. Faratin and J. Rodriguez-Aguilar, editors, Agent Mediated Electronic Commerce VI: Theories for and Engineering of Distributed Mechanisms and Systems (AMEC 2004), volume 3435 of Lecture Notes in Artificial Intelligence, pages 143-157, Berlin, 2005. Springer Verlag.
20. R. Schapire, P. Stone, D. McAllester, M. Littman, and J. Csirik. Modeling auction price uncertainty using boosting-based conditional density estimation, 2002.
21. P. R. Wurman, M. P. Wellman, and W. E. Walsh. The Michigan Internet AuctionBot: A configurable auction server for human and software agents. In K. P. Sycara and M. Wooldridge, editors, Proceedings of the 2nd International Conference on Autonomous Agents (Agents'98), pages 301-308, New York, 1998. ACM Press.