A prerequisite for social coordination is bidirectional communication between teammates, each playing two roles simultaneously: as receptive listeners and expressive speakers. For robots working with humans in complex situations with multiple goals that differ in importance, failure to fulfill the expectation of either role could undermine group performance due to misalignment of values between humans and robots. Specifically, a robot needs to serve as an effective listener to infer human users’ intents from instructions and feedback and as an expressive speaker to explain its decision processes to users. Here, we investigate how to foster effective bidirectional human-robot communications in the context of value alignment—collaborative robots and users form an aligned understanding of the importance of possible task goals. We propose an explainable artificial intelligence (XAI) system in which a group of robots predicts users’ values by taking in situ feedback into consideration while communicating their decision processes to users through explanations. To learn from human feedback, our XAI system integrates a cooperative communication model for inferring human values associated with multiple desirable goals. To be interpretable to humans, the system simulates human mental dynamics and predicts optimal explanations using graphical models. We conducted psychological experiments to examine the core components of the proposed computational framework. Our results show that real-time human-robot mutual understanding in complex cooperative tasks is achievable with a learning model based on bidirectional communication. We believe that this interaction framework can shed light on bidirectional value alignment in communicative XAI systems and, more broadly, in future human-machine teaming systems.

中文翻译：

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原位双向人机价值对齐

社交协调的先决条件是队友之间的双向沟通​​，每个人同时扮演两个角色：作为接受者的听众和富有表现力的演讲者。对于在具有不同重要性的多个目标的复杂情况下与人类一起工作的机器人，未能实现对任一角色的期望可能会由于人类和机器人之间的价值观不一致而破坏团队绩效。具体来说，机器人需要充当有效的倾听者，从指令和反馈中推断人类用户的意图，并充当富有表现力的扬声器，向用户解释其决策过程。在这里，我们研究如何在价值对齐的背景下促进有效的双向人机交流——协作机器人和用户对可能的任务目标的重要性形成一致的理解。我们提出了一个可解释的人工智能 (XAI) 系统，其中一组机器人通过考虑现场反馈来预测用户的价值，同时通过解释将他们的决策过程传达给用户。为了从人类反馈中学习，我们的 XAI 系统集成了一个合作沟通模型，用于推断与多个理想目标相关的人类价值。为了便于人类解释，该系统模拟人类心理动态并使用图形模型预测最佳解释。我们进行了心理实验来检查所提出的计算框架的核心组成部分。我们的结果表明，通过基于双向通信的学习模型可以实现复杂协作任务中的实时人机相互理解。