摘要翻译：
扑克对于人工智能来说是一个具有挑战性的问题，它具有不确定性、部分可观测性和未知对手的额外难度。对这一领域的所有不确定性进行建模并不是一件容易的事情。本文提出了一个贝叶斯概率模型，将博弈动态的不确定性与对手策略的不确定性相分离，用于求解一类广泛的扑克博弈。然后，我们描述了两个关键子问题的方法：(i)在给定先验分布和观察其博弈的情况下，推断对手策略的后验结果，(ii)对该分布做出适当的反应。我们在使用Dirichlet先验的简化版扑克上演示了整体方法，然后在使用更知情的先验的德克萨斯扑克的完整游戏上演示了整体方法。我们演示了基于后验的对对手进行有效反应的方法。
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英文标题：
《Bayes' Bluff: Opponent Modelling in Poker》
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作者：
Finnegan Southey, Michael P. Bowling, Bryce Larson, Carmelo Piccione,
  Neil Burch, Darse Billings, Chris Rayner
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最新提交年份：
2012
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Computer Science and Game Theory        计算机科学与博弈论
分类描述：Covers all theoretical and applied aspects at the intersection of computer science and game theory, including work in mechanism design, learning in games (which may overlap with Learning), foundations of agent modeling in games (which may overlap with Multiagent systems), coordination, specification and formal methods for non-cooperative computational environments. The area also deals with applications of game theory to areas such as electronic commerce.
涵盖计算机科学和博弈论交叉的所有理论和应用方面，包括机制设计的工作，游戏中的学习（可能与学习重叠），游戏中的agent建模的基础（可能与多agent系统重叠），非合作计算环境的协调、规范和形式化方法。该领域还涉及博弈论在电子商务等领域的应用。
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一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  Poker is a challenging problem for artificial intelligence, with non-deterministic dynamics, partial observability, and the added difficulty of unknown adversaries. Modelling all of the uncertainties in this domain is not an easy task. In this paper we present a Bayesian probabilistic model for a broad class of poker games, separating the uncertainty in the game dynamics from the uncertainty of the opponent's strategy. We then describe approaches to two key subproblems: (i) inferring a posterior over opponent strategies given a prior distribution and observations of their play, and (ii) playing an appropriate response to that distribution. We demonstrate the overall approach on a reduced version of poker using Dirichlet priors and then on the full game of Texas hold'em using a more informed prior. We demonstrate methods for playing effective responses to the opponent, based on the posterior.
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PDF链接：
https://arxiv.org/pdf/1207.1411