We consider the problem of providing users of deep Reinforcement Learning (RL) based systems with a better understanding of when their output can be trusted. We offer an explainable artificial intelligence (XAI) framework that provides a three-fold explanation: a graphical depiction of the systems generalization and performance in the current game state, how well the agent would play in semantically similar environments, and a narrative explanation of what the graphical information implies. We created a user-interface for our XAI framework and evaluated its efficacy via a human-user experiment. The results demonstrate a statistically significant increase in user trust and acceptance of the AI system with explanation, versus the AI system without explanation.
翻译:我们考虑了让深强化学习系统用户更好地了解何时可以相信其产出的问题,我们提出了一个可以解释的人工智能框架,它提供了三重解释:对当前游戏状态中的系统概括和性能的图形描述,该代理人在语义相似的环境中将发挥多大作用,对图形信息意味着什么的叙述性解释。我们为我们的 XAI 框架创建了一个用户界面,并通过人类用户实验评估其有效性。结果显示用户信任度和接受AI 系统时的解释在统计上显著提高,而AI 系统则没有解释。