Pluggable Social Artificial Intelligence for Enabling Human-Agent Teaming
This work addresses the problem of enabling effective human-agent teaming for developers and researchers in AI and human-computer interaction, but it is incremental as it builds on existing state-of-the-art concepts.
The paper tackles the need for new human-machine interaction concepts by proposing a framework and architecture for human-agent teaming, resulting in a proof-of-concept prototype that enables social collaboration between humans and machines as equal partners.
As intelligent systems are increasingly capable of performing their tasks without the need for continuous human input, direction, or supervision, new human-machine interaction concepts are needed. A promising approach to this end is human-agent teaming, which envisions a novel interaction form where humans and machines behave as equal team partners. This paper presents an overview of the current state of the art in human-agent teaming, including the analysis of human-agent teams on five dimensions; a framework describing important teaming functionalities; a technical architecture, called SAIL, supporting social human-agent teaming through the modular implementation of the human-agent teaming functionalities; a technical implementation of the architecture; and a proof-of-concept prototype created with the framework and architecture. We conclude this paper with a reflection on where we stand and a glance into the future showing the way forward.