共構成的タスク学習における自然言語対話を可能にする認知アーキテクチャへの展望
Towards a cognitive architecture to enable natural language interaction in co-constructive task learning
人間とロボットが協調してタスクを学ぶ共構成的タスク学習(CCTL)に自然言語を活かすため、認知アーキテクチャに必要な特性を整理し、複数分野の知見を統合した枠組みと残る課題を示した展望論文。
著者: Manuel Scheibl, Birte Richter, Alissa Müller, Michael Beetz, Britta Wrede
分類: cs.RO, cs.CL, cs.HC
原文アブストラクト
This research addresses the question, which characteristics a cognitive architecture must have to leverage the benefits of natural language in Co-Constructive Task Learning (CCTL). To provide context, we first discuss Interactive Task Learning (ITL), the mechanisms of the human memory system, and the significance of natural language and multi-modality. Next, we examine the current state of cognitive architectures, analyzing their capabilities to inform a concept of CCTL grounded in multiple sources. We then integrate insights from various research domains to develop a unified framework. Finally, we conclude by identifying the remaining challenges and requirements necessary to achieve CCTL in Human-Robot Interaction (HRI).
