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Speeding-up Poker Game Abstraction Computation: Average Rank Strength


 
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1. Title Title of document Speeding-up Poker Game Abstraction Computation: Average Rank Strength
 
2. Creator Author's name, affiliation, country Luís Filipe Guimarães Teófilo; University of Porto; Portugal
 
2. Creator Author's name, affiliation, country Luís Paulo Reis; University of Porto and University of Minho; Portugal
 
2. Creator Author's name, affiliation, country Henrique Lopes Cardoso; University of Porto; Portugal
 
3. Subject Discipline(s) poker;hand strength;abstraction;probabilities;cfr
 
3. Subject Keyword(s) Poker; Texas Hold'em; Average Rank Strength; Hand Strength; Abstraction; Nash Equilibrium; Extensive-form games
 
4. Description Abstract Some of the most successful Poker agents that participate in the Annual Computer Poker Competition (ACPC) use an almost zero regret strategy: a strategy that approximates a Nash Equilibrium. However, it is still unfeasible to efficiently compute a Nash Equilibrium without some sort of information set abstraction due to the size of Poker’s search tree. One popular technique for abstracting Poker information sets is to group hands with similar Expected Hand Strength (E[HS]) and thus play them in the same way. For large Poker variants, algorithms like CFR might need to calculate E[HS] billions of times, when the game abstraction is so large that it cannot be pre-computed, implying that E[HS] must be determined online. This way, improving the efficiency of this method would certainly reduce the computation time needed by CFR for these cases. In this paper we describe Average Rank Strength; a technique based on a pre-computed lookup table that speeds up E[HS] computation. Ours results demonstrate speed improvements of about three orders of magnitude and negligible results difference, when compared to the original E[HS].
 
5. Publisher Organizing agency, location
 
6. Contributor Sponsor(s) FCT – Fundação para a Ciência e a Tecnologia
 
7. Date (YYYY-MM-DD) 2013-06-29
 
8. Type Status & genre Peer-reviewed Paper
 
8. Type Type
 
9. Format File format PDF
 
10. Identifier Universal Resource Indicator https://www.aaai.org/ocs/index.php/WS/AAAIW13/paper/view/7083
 
11. Source Journal/conference title; vol., no. (year) AAAI Workshops; Workshops at the Twenty-Seventh AAAI Conference on Artificial Intelligence
 
12. Language English=en en
 
14. Coverage Geo-spatial location, chronological period, research sample (gender, age, etc.)
 
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