Adaptive machines have the potential to assist or interfere with human behavior in a range of contexts, from cognitive decision-making to physical device assistance. Therefore it is critical to understand how machine learning algorithms can influence human actions, particularly in situations where machine goals are misaligned with those of people. Since humans continually adapt to their environment using a combination of explicit and implicit strategies, when the environment contains an adaptive machine, the human and machine play a game. Game theory is an established framework for modeling interactions between two or more decision-makers that has been applied extensively in economic markets and machine algorithms. However, existing approaches make assumptions about, rather than empirically test, how adaptation by individual humans is affected by interaction with an adaptive machine. Here we tested learning algorithms for machines playing general-sum games with human subjects. Our algorithms enable the machine to select the outcome of the co-adaptive interaction from a constellation of game-theoretic equilibria in action and policy spaces. Importantly, the machine learning algorithms work directly from observations of human actions without solving an inverse problem to estimate the human's utility function as in prior work. Surprisingly, one algorithm can steer the human-machine interaction to the machine's optimum, effectively controlling the human's actions even while the human responds optimally to their perceived cost landscape. Our results show that game theory can be used to predict and design outcomes of co-adaptive interactions between intelligent humans and machines.

翻译：自适应机器有可能在从认知决策到物理设备辅助等一系列情境中辅助或干扰人类行为。因此，理解机器学习算法如何影响人类行动至关重要，尤其是在机器目标与人类目标不一致的情况下。由于人类会通过显式和隐式策略的结合不断适应环境，当环境中包含自适应机器时，人类与机器便展开了一场博弈。博弈论是一个用于建模两个或多个决策者之间交互的成熟框架，已广泛应用于经济市场和机器算法中。然而，现有方法仅对个体人类如何受自适应机器交互影响做出假设，而非通过实证检验。本研究针对人类受试者进行的通用和博弈，测试了机器的学习算法。我们的算法使机器能够从行动空间和政策空间中的一系列博弈论均衡中选择共适性交互的结果。重要的是，这些机器学习算法直接基于对人类行动的观察工作，无需像之前的工作那样通过求解逆问题来估计人类的效用函数。令人惊讶的是，一种算法能够将人机交互导向机器的最优结果，即使人类对其感知的成本景观做出最优响应，也能有效控制人类的行为。我们的结果表明，博弈论可用于预测和设计智能人类与机器之间共适性交互的结果。