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游戏的学生:完美和不完美的信息游戏的统一学习算法
Martin Schmid1,2, Matej Moravčík1,2, Neil Burch2,3,4
1EquiLibre Technologies, Prague, Czechia.
Science advances
|November 15, 2023
概括
游戏的学生是一个新的AI算法统一搜索,学习和游戏理论. 它在象棋,围棋,克和苏格兰庭院等复杂游戏中实现了最高性能,提升了一般AI能力.
科学领域:
- 人工智能的人工智能
- 游戏理论 游戏理论
- 机器学习 机器学习
背景情况:
- 从历史上看,游戏一直是推动人工智能 (AI) 发展的关键基准.
- 以前的AI方法在完美的信息游戏 (使用搜索和学习) 或不完美的信息游戏 (使用游戏理论推理和学习) 中表现出色.
研究的目的:
- 引入一个统一的,通用AI算法,能够处理完美和不完美的信息游戏.
- 在多种复杂游戏中展示强大的实证性能.
主要方法:
- 开发了"游戏的学生",一个新的算法,集成引导搜索,自动游戏机器学习和游戏理论推理.
- 从理论上验证了算法的稳定性和收性质.
- 在象棋,围棋,头顶无限制德克萨斯克和苏格兰场的经验评估表现.
主要成果:
- 游戏学生在大规模的完美和不完美的信息游戏中取得了强大的实证表现.
- 该算法证明了与增加的计算和近似能力的完美游戏的趋同.
- 取得了最先进的成绩,包括在克和苏格兰场击败顶级经纪人,以及在象棋和围棋中表现出色.
结论:
- 游戏的学生代表了向任意环境的一般AI算法迈出的重要一步.
- 统一的方法有效地利用指导式搜索,学习和游戏理论推理来实现卓越的性能.
- 该算法的成功突出了整合各种AI技术用于复杂的战略决策的潜力.
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