Balancing safety and exploitability in opponent modeling

Proceedings of the National Conference on Artificial Intelligence

Volumn 2, Issue , 2011, Pages 1515-1520

  Wang, Zhikun ^a   Boularias, Abdeslam ^a   Mülling, Katharina ^a   Peters, Jan ^a  

^a MAX PLANCK INSTITUTE FOR INTELLIGENT SYSTEMS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

FINITE NUMBER; HIGH PROBABILITY; HUMAN PLAYERS; MINIMAX; MODELING TECHNIQUE; OPPONENT MODELING; REPEATED GAMES; RISK REDUCTIONS; STOCHASTIC GAME; TABLE-TENNIS;

ARTIFICIAL INTELLIGENCE;

EID: 80055026439     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (12)

1. Bowling, M., and Veloso, M. 2002. Multiagent learning using a variable learning rate. Artificial Intelligence 136(2):215-250. (Pubitemid 34232184)
2. Bowling, M. 2005. Convergence and no-regret in multiagent learning. In Advances in neural information processing systems 17, 209.
3. Brown, G. 1951. Iterative solution of games by fictitious play. Activity analysis of production and allocation 13(1):374-376.
4. Conitzer, V., and Sandholm, T. 2007. AWESOME: A general multiagent learning algorithm that converges in selfplay and learns a best response against stationary opponents. Machine Learning 67(1):23-43. (Pubitemid 46571918)
5. Johanson, M., and Bowling, M. 2009. Data biased robust counter strategies. In Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics.
6. Markovitch, S., and Reger, R. 2005. Learning and exploiting relative weaknesses of opponent agents. Autonomous Agents and Multi-Agent Systems 10(2):103-130.
7. McCracken, P., and Bowling, M. 2004. Safe strategies for agent modelling in games. In AAAI Fall Symposium on Artificial Multi-agent Learning, 103-110.
8. Muelling, K.; Kober, J.; and Peters, J. 2010. A Biomimetic Approach to Robot Table Tennis. In Proceedings of IROS.
9. Powers, R.; Shoham, Y.; and Vu, T. 2007. A general criterion and an algorithmic framework for learning in multi-agent systems. Machine Learning 67(1):45-76.
10. Seldin, Y., and Tishby, N. 2010. PAC-Bayesian Analysis of Co-clustering and Beyond. Journal of Machine Learning Research 11:3595-3646.
11. Simon, H. 1991. Bounded rationality and organizational learning. Organization science 2(1):125-134.
12. Zinkevich, M. 2003. Online convex programming and generalized infinitesimal gradient ascent. In Proceedings of the International Conference on Machine Learning.