Game-Tree Search with Adaptation in Stochastic Imperfect-Information Games

Building a high-performance poker-playing program is a challenging project. The best program to date, PsOpti, uses game theory to solve a simplified version of the game. Although the program plays reasonably well, it is oblivious to the opponent’s weaknesses and biases. Modeling the opponent to expl...

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Hauptverfasser: Billings, Darse, Davidson, Aaron, Schauenberg, Terence, Burch, Neil, Bowling, Michael, Holte, Robert, Schaeffer, Jonathan, Szafron, Duane
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container_start_page 21
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creator Billings, Darse
Davidson, Aaron
Schauenberg, Terence
Burch, Neil
Bowling, Michael
Holte, Robert
Schaeffer, Jonathan
Szafron, Duane
description Building a high-performance poker-playing program is a challenging project. The best program to date, PsOpti, uses game theory to solve a simplified version of the game. Although the program plays reasonably well, it is oblivious to the opponent’s weaknesses and biases. Modeling the opponent to exploit predictability is critical to success at poker. This paper introduces Vexbot, a program that uses a game-tree search algorithm to compute the expected value of each betting option, and does real-time opponent modeling to improve its evaluation function estimates. The result is a program that defeats PsOpti convincingly, and poses a much tougher challenge for strong human players.
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identifier ISSN: 0302-9743
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source Springer Books
subjects Applied sciences
Computer science
control theory
systems
Computer systems and distributed systems. User interface
Decision Node
Exact sciences and technology
Game Tree
Information systems. Data bases
Leaf Node
Memory organisation. Data processing
Nash Equilibrium
Nash Equilibrium Strategy
Software
title Game-Tree Search with Adaptation in Stochastic Imperfect-Information Games
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