通过将语言模型与战略推理相结合,在外交游戏中发挥人类水平
, Anton Bakhtin1, Noam Brown1
1Meta AI, 1 Hacker Way, Menlo Park, CA, USA.
概括
在复杂的外交游戏中, 这种人工智能在自然语言谈判和战略协调方面表现出色,
科学领域:
- 人工智能
- 自然语言处理
- 游戏理论
背景情况:
- 训练人工智能系统模仿人类语言已经取得了重大进展.
- 构建在交互环境中故意与人类沟通的AI代理仍然是一个挑战.
研究的目的:
- 在策略游戏"外交"中介绍Cicero,一个被设计为人类水平的AI代理.
- 证明Cicero能够将语言模型与计划和强化学习相结合,以进行谈判和协调.
主要方法:
- 西塞罗将语言模型与规划和强化学习算法整合在一起.
- 人工智能从对话中推断出玩家的信念和意图.
- 为了支持其战略计划,
主要成果:
- 在外交方面取得了人类水平的成绩,
- 在40场比赛中,西塞罗比人类球员的平均分数翻了一番.
- 在网络外交联盟中排名前10%.
结论:
- 在创造有意沟通和战略互动的能力的人工智能代理方面,
- 人工智能在外交方面的成功凸显了将先进的语言模型与复杂任务的规划和学习相结合的潜力.
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