Advancing Social Intelligence in AI Agents: Technical Challenges and Open Questions

Abstract

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.