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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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. |
doi_str_mv | 10.1007/11674399_2 |
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issn | 0302-9743 1611-3349 |
language | eng |
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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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