A Hierarchical Framework for Collaborative Artificial Intelligence

TL;DR

This paper introduces a hierarchical framework for collaborative AI systems, organizing research challenges by activity and information sharing, and discusses approaches, issues, and societal impacts of such systems.

Contribution

It presents a novel hierarchical framework for collaborative AI, integrating classical and machine learning approaches, and addresses cross-cutting communication and social challenges.

Findings

Framework organizes research challenges effectively

Highlights importance of explanation and social aspects

Identifies key research challenges and societal impacts

Abstract

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.