This study developed an explainable AI for ship collision avoidance. Initially, a critic network composed of sub-task critic networks was proposed to individually evaluate each sub-task in collision avoidance to clarify the AI decision-making processes involved. Additionally, an attempt was made to discern behavioral intentions through a Q-value analysis and an Attention mechanism. The former focused on interpreting intentions by examining the increment of the Q-value resulting from AI actions, while the latter incorporated the significance of other ships in the decision-making process for collision avoidance into the learning objective. AI's behavioral intentions in collision avoidance were visualized by combining the perceived collision danger with the degree of attention to other ships. The proposed method was evaluated through a numerical experiment. The developed AI was confirmed to be able to safely avoid collisions under various congestion levels, and AI's decision-making process was rendered comprehensible to humans. The proposed method not only facilitates the understanding of DRL-based controllers/systems in the ship collision avoidance task but also extends to any task comprising sub-tasks.

翻译：本研究开发了一种面向船舶避碰的可解释人工智能。首先，提出了一种由子任务评论家网络组成的评论家网络，用于分别评估避碰中的每个子任务，以厘清所涉及的人工智能决策过程。此外，通过Q值分析和注意力机制尝试辨别行为意图。前者侧重于通过检查人工智能行为导致的Q值增量来解读意图，而后者则将其他船舶在避碰决策过程中的重要性纳入学习目标。通过结合感知到的碰撞危险与对其他船舶的关注程度，人工智能在避碰中的行为意图得以可视化。通过数值实验对所提方法进行了评估。经确认，所开发的人工智能能够在各种拥堵程度下安全避碰，并且人工智能的决策过程变得可被人类理解。所提方法不仅有助于理解基于深度强化学习的船舶避碰任务控制器/系统，还可推广至任何包含子任务的任务。