Nonverbal Human Signals Can Help Autonomous Agents Infer Human Preferences for Their Behavior

An overarching goal of Artificial Intelligence (AI) is creating autonomous, social agents that help people. Two important challenges, though, are that different people prefer different assistance from agents and that preferences can change over time. Thus, helping behaviors should be tailored to how an individual feels during the interaction. We hypothesize that human nonverbal behavior can give clues about users' preferences for an agent's helping behaviors, augmenting an agent's ability to computationally predict such preferences with machine learning models. To investigate our hypothesis, we collected data from 194 participants via an online survey in which participants were recorded while playing a multiplayer game. We evaluated whether the inclusion of nonverbal human signals, as well as additional context (e.g., via game or personality information), led to improved prediction of user preferences between agent behaviors compared to explicitly provided survey responses. Our results suggest that nonverbal communication -- a common type of human implicit feedback -- can aid in understanding how people want computational agents to interact with them.