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.

构建社交智能 AI 代理（Social-AI）是一个跨学科、多模态的研究目标，涉及创建能够感知、理解、推理、学习并回应其他代理（人类或人工）的情感、行为和认知的代理。过去十年，在自然语言处理、机器学习、机器人技术、人机交互、计算机视觉和语音等几个计算社区中，社交智能 AI 取得了进展。这篇立场论文中，我们确定了一组潜在的技术挑战和开放性问题，以促进社交智能 AI 的研究，并结合社交智能概念和以往的研究进展进行讨论。