Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Abstraction pathologies in extensive games
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Approximating game-theoretic optimal strategies for full-scale poker
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Probabilistic state translation in extensive games with large action sets
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Gradient-based algorithms for finding Nash equilibria in extensive form games
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
| Hi-index | 0.00 |
There has been significant recent interest in computing good strategies for large games. Most prior work involves computing an approximate equilibrium strategy in a smaller abstract game, then playing this strategy in the full game. In this paper, we present a modification of this approach that works by constructing a deterministic strategy in the full game from the solution to the abstract game; we refer to this procedure as purification. We show that purification, and its generalization which we call thresholding, lead to significantly stronger play than the standard approach in a wide variety of experimental domains. One can view these approaches as ways of achieving robustness against one's own lossy abstraction.