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。

AI 代理人中的社交智能提升：技术挑战与未解之谜

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

Ontologies play a critical role in Semantic Web technologies by providing a
structured and standardized way to represent knowledge and enabling machines to
understand the meaning of data. Several taxonomies and ontologies have been
generated, but individuals target one domain, and only some of those have been
found expensive in time and manual effort. Also, they need more coverage of
unconventional topics representing a more holistic and comprehensive view of
the knowledge landscape and interdisciplinary collaborations. Thus, there needs
to be an ontology covering Science and Technology and facilitate
multidisciplinary research by connecting topics from different fields and
domains that may be related or have commonalities. To address these issues, we
present an automatic Science and Technology Ontology (S&TO) that covers
unconventional topics in different science and technology domains. The proposed
S&TO can promote the discovery of new research areas and collaborations across
disciplines. The ontology is constructed by applying BERTopic to a dataset of
393,991 scientific articles collected from Semantic Scholar from October 2021
to August 2022, covering four fields of science. Currently, S&TO includes 5,153
topics and 13,155 semantic relations. S&TO model can be updated by running
BERTopic on more recent datasets

该研究介绍了一个包含科学技术领域中非传统主题的自动化科学技术本体，并通过连接不同领域和专业之间相关或具有共性的主题来促进跨学科研究与合作。使用从 2021 年 10 月到 2022 年 8 月从语义学者 (Semantic Scholar) 收集的 393,991 篇科学文章构建 S＆TO。目前，S&TO 划分了四个科学领域中的 5153 个主题和 13155 个语义关系，可以通过对更新的数据集运行 BERTopic 来更新模型。