Artificial Intelligence - Chips challenging champions: games, computers and Artificial Intelligence
Excessive Gap Technique in Nonsmooth Convex Minimization
SIAM Journal on Optimization
Finding equilibria in large sequential games of imperfect information
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Algorithms and assessment in computer poker
Algorithms and assessment in computer poker
A near-optimal strategy for a heads-up no-limit Texas Hold'em poker tournament
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Prob-Maxn: playing N-player games with opponent models
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
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Game-Tree search with adaptation in stochastic imperfect-information games
CG'04 Proceedings of the 4th international conference on Computers and Games
Lossless abstraction of imperfect information games
Journal of the ACM (JACM)
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Computing an approximate jam/fold equilibrium for 3-player no-limit Texas Hold'em tournaments
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems: demo papers
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 3
Gradient-based algorithms for finding Nash equilibria in extensive form games
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
Smoothing Techniques for Computing Nash Equilibria of Sequential Games
Mathematics of Operations Research
Using counterfactual regret minimization to create competitive multiplayer poker agents
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
Game theory-based opponent modeling in large imperfect-information games
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Lossy stochastic game abstraction with bounds
Proceedings of the 13th ACM Conference on Electronic Commerce
Accelerating best response calculation in large extensive games
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
New results on the verification of Nash refinements for extensive-form games
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Efficient Nash equilibrium approximation through Monte Carlo counterfactual regret minimization
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Online implicit agent modelling
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Evaluating state-space abstractions in extensive-form games
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Hi-index | 0.00 |
We present a new abstraction algorithm for sequential imperfect information games. While most prior abstraction algorithms employ a myopic expected-value computation as a similarity metric, our algorithm considers a higher-dimensional space consisting of histograms over abstracted classes of states from later stages of the game. This enables our bottom-up abstraction algorithm to automatically take into account potential: a hand can become relatively better (or worse) over time and the strength of different hands can get resolved earlier or later in the game. We further improve the abstraction quality by making multiple passes over the abstraction, enabling the algorithm to narrow the scope of analysis to information that is relevant given abstraction decisions made for earlier parts of the game. We also present a custom indexing scheme based on suit isomorphisms that enables one to work on significantly larger models than before. We apply the techniques to heads-up limit Texas Hold'em poker. Whereas all prior game theory-based work for Texas Hold'em poker used generic off-the-shelf linear program solvers for the equilibrium analysis of the abstracted game, we make use of a recently developed algorithm based on the excessive gap technique from convex optimization. This paper is, to our knowledge, the first to abstract and game-theoretically analyze all four betting rounds in one run (rather than splitting the game into phases). The resulting player, GS3, beats BluffBot, GS2, Hyperborean, Monash-BPP, Sparbot, Teddy, and Vexbot, each with statistical significance. To our knowledge, those competitors are the best prior programs for the game.