Since Alan Turing envisioned artificial intelligence, technical progress has often been measured by the ability to defeat humans in zero-sum encounters (e.g., Chess, Poker, or Go). Less attention has been given to scenarios in which human-machine cooperation is beneficial but non-trivial, such as scenarios in which human and machine preferences are neither fully aligned nor fully in conflict. Cooperation does not require sheer computational power, but instead is facilitated by intuition, cultural norms, emotions, signals, and pre-evolved dispositions. Here, we develop an algorithm that combines a state-of-the-art reinforcement-learning algorithm with mechanisms for signaling. We show that this algorithm can cooperate with people and other algorithms at levels that rival human cooperation in a variety of two-player repeated stochastic games. These results indicate that general human-machine cooperation is achievable using a non-trivial, but ultimately simple, set of algorithmic mechanisms.

中文翻译：

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与机器合作。

自从艾伦·图灵（Alan Turing）设想人工智能以来，技术进步通常是通过在零和遭遇（例如国际象棋，扑克或围棋）中击败人类的能力来衡量的。人们对人机合作有益但不平凡的场景的关注较少，例如人机偏好不完全一致也不完全冲突的场景。合作并不需要纯粹的计算能力，而是可以通过直觉，文化规范，情感，信号和预先发展的倾向来促进。在这里，我们开发了一种算法，该算法结合了最新的强化学习算法和信令机制。我们证明了该算法可以与人和其他算法进行协作，并且可以在各种两人重复的随机游戏中与人的协作相媲美。