Reinforcement learning techniques successfully generate convincing agent behaviors, but it is still difficult to tailor the behavior to align with a user's specific preferences. What is missing is a communication method for the system to explain the behavior and for the user to repair it. In this paper, we present a novel interaction method that uses interactive explanations using templates of natural language as a communication method. The main advantage of this interaction method is that it enables a two-way communication channel between users and the agent; the bot can explain its thinking procedure to the users, and the users can communicate their behavior preferences to the bot using the same interactive explanations. In this manner, the thinking procedure of the bot is transparent, and users can provide corrections to the bot that include a suggested action to take, a goal to achieve, and the reasons behind these decisions. We tested our proposed method in a clone of the video game named \textit{Super Mario Bros.}, and the results demonstrate that our interactive explanation approach is effective at diagnosing and repairing bot behaviors.
翻译:强化学习技术成功地产生了令人信服的代理行为, 但根据用户的具体偏好调整行为仍很难。 缺少的是系统解释行为和用户修复行为的沟通方法。 在本文中, 我们展示了一种新型互动方法, 使用天然语言模板的互动解释作为沟通方法。 这种互动方法的主要优点在于它能够让用户和代理之间进行双向沟通渠道; 机器人可以向用户解释其思维程序, 用户可以使用相同的互动解释向机器人传达其行为偏好。 这样, 机器人的思维程序是透明的, 用户可以提供对机器人的更正, 其中包括建议采取的行动、 实现目标以及这些决定背后的原因。 我们用名为\ textit{ Super Mario Bross.} 的视频游戏克隆测试了我们提议的方法, 结果表明我们的互动解释方法在诊断和修复机器人行为方面是有效的。