Most Important Fundamental Rule of Poker Strategy
June 08, 2019 Β· Declared Dead Β· π The Florida AI Research Society
"No code URL or promise found in abstract"
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Authors
Sam Ganzfried, Max Chiswick
arXiv ID
1906.09895
Category
cs.AI: Artificial Intelligence
Cross-listed
cs.GT,
cs.LG,
econ.TH
Citations
3
Venue
The Florida AI Research Society
Last Checked
4 months ago
Abstract
Poker is a large complex game of imperfect information, which has been singled out as a major AI challenge problem. Recently there has been a series of breakthroughs culminating in agents that have successfully defeated the strongest human players in two-player no-limit Texas hold 'em. The strongest agents are based on algorithms for approximating Nash equilibrium strategies, which are stored in massive binary files and unintelligible to humans. A recent line of research has explored approaches for extrapolating knowledge from strong game-theoretic strategies that can be understood by humans. This would be useful when humans are the ultimate decision maker and allow humans to make better decisions from massive algorithmically-generated strategies. Using techniques from machine learning we have uncovered a new simple, fundamental rule of poker strategy that leads to a significant improvement in performance over the best prior rule and can also easily be applied by human players.
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