Past research has clearly established that music can affect mood and that mood affects emotional and cognitive processing, and thus decision-making. It follows that if a robot interacting with a person needs to predict the person's behavior, knowledge of the music the person is listening to when acting is a potentially relevant feature. To date, however, there has not been any concrete evidence that a robot can improve its human-interactive decision-making by taking into account what the person is listening to. This research fills this gap by reporting the results of an experiment in which human participants were required to complete a task in the presence of an autonomous agent while listening to background music. Specifically, the participants drove a simulated car through an intersection while listening to music. The intersection was not empty, as another simulated vehicle, controlled autonomously, was also crossing the intersection in a different direction. Our results clearly indicate that such background information can be effectively incorporated in an agent's world representation in order to better predict people's behavior. We subsequently analyze how knowledge of music impacted both participant behavior and the resulting learned policy.\setcounter{footnote}{2}\footnote{An earlier version of part of the material in this paper appeared originally in the first author's Ph.D. Dissertation~\cite{liebman2020sequential} but it has not appeared in any pear-reviewed conference or journal.}

翻译：过往研究已明确证实，音乐能够影响情绪，而情绪又作用于情感与认知加工，进而影响决策过程。由此推论，若与人类交互的机器人需要预测人的行为，那么当个体采取行动时所聆听的音乐信息便可能成为具有相关性的特征。然而迄今为止，尚无具体证据表明机器人可以通过考虑人类当前所听的音乐来优化其交互决策。本研究通过报告一项实验的结果填补了这一空白：实验中，人类参与者在存在自主智能体的情境下需完成一项任务，且同时聆听背景音乐。具体而言，参与者在驾车穿越十字路口时聆听音乐，该路口并非空无一物——另一辆由自主系统控制的模拟车辆正从不同方向穿过同一路口。我们的结果清晰表明，此类背景信息可有效整合进智能体的世界表征中，从而更精准地预测人类行为。随后，我们分析了音乐信息对参与者行为及最终习得策略的具体影响。