Using computer poker as a testbed for solving multiagent decision problems

使用计算机扑克作为解决多智能体决策问题的测试平台

• 批准号: 8191-2011
• 负责人: Szafron, Duane
• 金额: $ 2.11万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=508260
• 关键词: Using; computer; poker; testbed; solving

Games are used to advance multi-agent decision-making techniques. Many games are deterministic (no chance) with perfect information (all info visible to all agents) like Chess and Checkers. However, many real-world scenarios with competing agents are non-deterministic with imperfect information. For two-player zero-sum perfect recall games, a recent technique called Counterfactual Regret Minimization (CFR) computes strategies that are provably convergent to an epsilon-Nash equilibrium. A Nash equilibrium strategy is useful in two-player games since it maximizes its utility against a worst-case opponent.

游戏被用来推进多智能体决策技术。许多游戏是确定性的（没有机会），具有完美的信息（所有信息对所有代理可见），如国际象棋和机器人。然而，许多现实世界中的场景与竞争代理是不确定的不完美的信息。对于两个玩家的零和完美回忆游戏，最近的技术称为反事实遗憾最小化（CFR）计算可证明收敛到ε-纳什均衡的策略。纳什均衡策略在两人博弈中是有用的，因为它能在最坏情况下最大化对手的效用。