Introduction to the special issue on learning and computational game theory

• Bowling, M., & Veloso, M. (2002). Multiagent learning using a variable learning rate. Artificial Intelligence, 136, 215–250.

  Article  MATH  MathSciNet  Google Scholar 

• Dalal, S. R., Fowlkes, E. B., & Hoadley, B. (1989). Risk analysis of the space shuttle: Pre-Challenger prediction of failure. Journal of the American Statistical Association, 84.

• Daskalakis, C., Goldberg, P. W., & Papadimitriou, C. H. (2006). The complexity of computing a nash equilibrium. In Proceedings of the Thirty-Eighth Annual ACM Symposium on Theory of Computing (pp. 71–78).

• Foster, D., & Vohra, R. (1997). Calibrated learning and correlated equilibrium. Games and Economic Behavior, 21, 40–55.

  Article  MATH  MathSciNet  Google Scholar 

• Greenwald, A., Li, Z., & Marks, C. (2006). Bounds for regret-matching algorithms. In Proceedings of the Ninth International Symposium on Artificial Intelligence and Mathematics.

• Kearns, M., Littman, M. L., & Singh, S. (2001). Graphical models for game theory. In Proceedings of the 17th Conference on Uncertainty in Artificial Intelligence (UAI) (pp. 253–260).

• Shoham, Y., Powers, R., & Grenager, T. (2007). If multi-agent learning is the answer, what is the question? Artificial Intelligence. Special issue on the foundations of research in multi-agent learning.

Download references