Agendas for multi-agent learning

Shoham, 2007, If multi-agent learning is the answer, what is the question?, Artificial Intelligence, 171, 365, 10.1016/j.artint.2006.02.006 Billings, 2003, Approximating game-theoretic optimal strategies for full-scale poker Shi, 2001, Abstraction methods for game theoretic poker, vol. 2063, 333 R. Emery-Montemerlo, G. Gordon, J. Schneider, S. Thrun, Game theoretic control for robot teams, in: Proc. Conf. on Robotics and Automation (ICRA), 2005 C. Bererton, Multi-robot coordination and competition using mixed integer and linear programs, PhD thesis, Carnegie Mellon Robotics Institute, 2004. Available as tech report CMU-RI-TR-04-65 C. Guestrin, G. Gordon, Distributed planning in hierarchical factored MDPs, in: A. Darwiche, N. Friedman (Eds.), Uncertainty in Artificial Intelligence (UAI), vol. 18, 2002 Stone, 2005, Reinforcement learning for RoboCup-soccer keepaway, Adaptive Behavior, 13, 165, 10.1177/105971230501300301 Bowling, 2003, Simultaneous adversarial multi-robot learning Cheng, 2005, Walverine: A Walrasian trading agent, Decision Support Systems, 39, 169, 10.1016/j.dss.2003.10.005 D. Pardoe, P. Stone, TacTex-2005: A champion supply chain management agent, in: Proceedings of the Twenty-First National Conference on Artificial Intelligence, July 2006 Stone, 2001, ATTac-2000: An adaptive autonomous bidding agent, Journal of Artificial Intelligence Research, 15, 189, 10.1023/A:1011018426725 Kreps, 1990 Kalai, 1993, Rational learning leads to Nash equilibrium, Econometrica, 61, 1019, 10.2307/2951492 Foster, 1999, Regret in the on-line decision problem, Games and Economic Behavior, 29, 7, 10.1006/game.1999.0740 Singh, 2000, Nash convergence of gradient dynamics in general-sum games, 541 R. Powers, Y. Shoham, New criteria and a new algorithm for learning in multi-agent systems, in: Advances in Neural Information Processing Systems, vol. 17, 2005 G.J. Gordon, Agendas for multi-agent learning, Technical Report CMU-ML-06-116, Carnegie Mellon University, 2006 Gordon Brafman, 2004, Efficient learning equilibrium, Artificial Intelligence, 159 C. Murray, G.J. Gordon, Multi-robot negotiation: approximating the set of subgame perfect equilibria in general-sum stochastic games, in: Advances in Neural Information Processing Systems, vol. 19, 2007 Rubinstein, 1982, Perfect equilibrium in a bargaining model, Econometrica, 50, 97, 10.2307/1912531 Nash, 1950, The bargaining problem, Econometrica, 18, 155, 10.2307/1907266 G.J. Gordon, Approximate solutions to Markov decision processes, PhD thesis, Carnegie Mellon University, 1999 Zinkevich, 2003, Online convex programming and generalized infinitesimal gradient ascent G.J. Gordon, No-regret algorithms for online convex programs, in: Advances in Neural Information Processing Systems, vol. 19, 2007 A. Kalai, S. Vempala, Geometric algorithms for online optimization, Technical Report MIT-LCS-TR-861, Massachusetts Institute of Technology, 2002