Ways of Thinking: The Limits of Rational Thought and Artificial Intelligence
Ways of Thinking: The Limits of Rational Thought and Artificial Intelligence
Behind Deep Blue: Building the Computer that Defeated the World Chess Champion
Behind Deep Blue: Building the Computer that Defeated the World Chess Champion
A Texas Hold'em poker player based on automated abstraction and real-time equilibrium computation
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Approximating game-theoretic optimal strategies for full-scale poker
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Computer science and game theory
Communications of the ACM - Designing games with a purpose
Strategy evaluation in extensive games with importance sampling
Proceedings of the 25th international conference on Machine learning
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Effective short-term opponent exploitation in simplified poker
Machine Learning
Abstraction pathologies in extensive games
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
First-order algorithm with O(ln(1/ε )) convergence for ε -equilibrium in two-person zero-sum games
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Game theory pragmatics: a challenge for AI
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 3
Strategy exploration in empirical games
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Speeding up gradient-based algorithms for sequential games
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Artificial Intelligence
Double-oracle algorithm for computing an exact nash equilibrium in zero-sum extensive-form games
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
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In normal scenarios, computer scientists often consider the number of states in a game to capture the difficulty of learning an equilibrium. However, players do not see games in the same light: most consider Go or Chess to be more complex than Monopoly. In this paper, we discuss a new measure of game complexity that links existing state-of-the-art algorithms for computing approximate equilibria to a more human measure. In particular, we consider the range of skill in a game, i.e. how many different skill levels exist. We then modify existing techniques to design a new algorithm to compute approximate equilibria whose performance can be captured by this new measure. We use it to develop the first near Nash equilibrium for a four round abstraction of poker, and show that it would have been able to win handily the bankroll competition from last year's AAAI poker competition.