In cooperation, the workers must know how co-workers behave. However, an
agent's policy, which is embedded in a statistical machine learning model, is
hard to understand, and requires much time and knowledge to comprehend.
Therefore, it is difficult for people to predict the behavior of machine
learning robots, which makes Human Robot Cooperation challenging. In this
paper, we propose Instruction-based Behavior Explanation (IBE), a method to
explain an autonomous agent's future behavior. In IBE, an agent can
autonomously acquire the expressions to explain its own behavior by reusing the
instructions given by a human expert to accelerate the learning of the agent's
policy. IBE also enables a developmental agent, whose policy may change during
the cooperation, to explain its own behavior with sufficient time granularity.

本文提出了一种基于指令的行为解释（IBE）方法，它可以帮助人类理解机器学习机器人的行为，促进人机协作。IBE 允许代理自主获取表达来解释自己的行为，并通过重复使用人类专家给出的指令来加速代理政策的学习。IBE 还允许一个发展中的代理向人类解释其行为，以便在合作期间更好地进行沟通。