Advancing Social Intelligence in AI Agents: Technical Challenges and
  Open Questions
Leena Mathur Paul Pu Liang Louis-Philippe Morency
Building socially-intelligent AI agents (Social-AI) is a multidisciplinary,
multimodal research goal that involves creating agents that can sense,
perceive, reason about, learn from, and respond to affect, behavior, and
cognition of other agents (human or artificial). Progress towards Social-AI has
accelerated in the past decade across several computing communities, including
natural language processing, machine learning, robotics, human-machine
interaction, computer vision, and speech. Natural language processing, in
particular, has been prominent in Social-AI research, as language plays a key
role in constructing the social world. In this position paper, we identify a
set of underlying technical challenges and open questions for researchers
across computing communities to advance Social-AI. We anchor our discussion in
the context of social intelligence concepts and prior progress in Social-AI
research.