内在的・外在的説明性の統合：ヒューマンロボットインタラクションにおけるニューラルネットワーク理解の重要性
Integrating Intrinsic and Extrinsic Explainability: The Relevance of Understanding Neural Networks for Human-Robot Interaction
ヒューマノイドロボットにXAIを適用する際の課題を整理し、ロボット自身による内在的説明と環境からの外在的説明の相互作用で効率的な行動を実現するオープンソースロボットNICOを紹介する位置づけ論文。
著者: Tom Weber, Stefan Wermter
分類: cs.RO, cs.AI
原文アブストラクト
Explainable artificial intelligence (XAI) can help foster trust in and acceptance of intelligent and autonomous systems. Moreover, understanding the motivation for an agent's behavior results in better and more successful collaborations between robots and humans. However, not only can humans benefit from a robot's explanation but the robot itself can also benefit from explanations given to him. Currently, most attention is paid to explaining deep neural networks and black-box models. However, a lot of these approaches are not applicable to humanoid robots. Therefore, in this position paper, current problems with adapting XAI methods to explainable neurorobotics are described. Furthermore, NICO, an open-source humanoid robot platform, is introduced and how the interaction of intrinsic explanations by the robot itself and extrinsic explanations provided by the environment enable efficient robotic behavior.
