創発：能動的問い合わせによる非対称身体性エージェントの特権情報バイアス克服
Emergence: Overcoming Privileged Information Bias in Asymmetric Embodied Agents via Active Querying
AI2-THOR内で知識を持つLeaderとセンサー制限のあるFollowerの協調を検証し、能動的問い合わせが特権情報バイアスを軽減することを示した研究。
著者: Shaun Baek, Sam Liu, Joseph Ukpong
分類: cs.AI, cs.MA, cs.RO
原文アブストラクト
Large Language Models (LLMs) act as powerful reasoning engines but struggle with "symbol grounding" in embodied environments, particularly when information is asymmetrically distributed. We investigate the Privileged Information Bias (or "Curse of Knowledge"), where a knowledgeable "Leader" agent fails to guide a sensor-limited "Follower" due to a lack of Theory of Mind. To quantify this phenomenon, we propose a novel Asymmetric Assistive Reasoning framework within AI2-THOR. Our experiments reveal a significant "Success Gap": while the Leader successfully perceives the target in 35.0% of episodes, the collaborative team succeeds only 17.0% of the time, implying that nearly 50% of feasible plans fail solely due to communicative grounding errors. We demonstrate that a "Pull-based" protocol (active querying) is significantly more robust than standard "Push-based" instruction, with successful episodes featuring 2x the frequency of clarification requests. This research isolates the mechanism of active uncertainty reduction as a prerequisite for safe human-AI and robot-robot collaboration.