協調的人工知能のための階層的フレームワーク
A Hierarchical Framework for Collaborative Artificial Intelligence
協調的知能システムの研究課題を階層的に整理し、各レベルでの古典的工学手法と機械学習ベースの現代的手法をレビューし、説明や社会的側面などの横断的課題を議論する。
著者: James L. Crowley, Joëlle L Coutaz, Jasmin Grosinger, Javier Vázquez-Salceda, Cecilio Angulo, Alberto Sanfeliu, Luca Iocchi, Anthony G. Cohn
分類: cs.AI, cs.HC, cs.MA, cs.RO
原文アブストラクト
We propose a hierarchical framework for collaborative intelligent systems. This framework organizes research challenges based on the nature of the collaborative activity and the information that must be shared, with each level building on capabilities provided by lower levels. We review research paradigms at each level, with a description of classical engineering-based approaches and modern alternatives based on machine learning, illustrated with a running example using a hypothetical personal service robot. We discuss cross-cutting issues that occur at all levels, focusing on the problem of communicating and sharing comprehension, the role of explanation and the social nature of collaboration. We conclude with a summary of research challenges and a discussion of the potential for economic and societal impact provided by technologies that enhance human abilities and empower people and society through collaboration with Intelligent Systems.
