Kovač Grgur, Portelas Rémy, Dominey Peter Ford, Oudeyer Pierre-Yves
Flowers Team, INRIA, Bordeaux, France.
Ubisoft La Forge, Bordeaux, France.
Front Neurorobot. 2024 Oct 9;18:1396359. doi: 10.3389/fnbot.2024.1396359. eCollection 2024.
Developmental psychologists have long-established socio-cognitive abilities as fundamental to human intelligence and development. These abilities enable individuals to enter, learn from, and contribute to a surrounding culture. This drives the process of cumulative cultural evolution, which is responsible for humanity's most remarkable achievements. AI research on social interactive agents mostly concerns the of culture in a multi-agent setting (often without a strong grounding in developmental psychology). We argue that AI research should be informed by psychology and study socio-cognitive abilities enabling to a culture as well. We draw inspiration from the work of Michael Tomasello and Jerome Bruner, who studied socio-cognitive development and emphasized the influence of a cultural environment on intelligence. We outline a broader set of concepts than those currently studied in AI to provide a foundation for research in artificial social intelligence. Those concepts include social cognition (joint attention, perspective taking), communication, social learning, formats, and scaffolding. To facilitate research in this domain, we present The SocialAI school-a tool that offers a customizable parameterized suite of procedurally generated environments. This tool simplifies experimentation with the introduced concepts. Additionally, these environments can be used both with multimodal RL agents, or with pure-text Large Language Models (LLMs) as interactive agents. Through a series of case studies, we demonstrate the versatility of the SocialAI school for studying both RL and LLM-based agents. Our motivation is to engage the AI community around social intelligence informed by developmental psychology, and to provide a user-friendly resource and tool for initial investigations in this direction. Refer to the project website for code and additional resources: https://sites.google.com/view/socialai-school.
发展心理学家长期以来一直将社会认知能力确立为人类智力和发展的基础。这些能力使个体能够融入周围的文化,从中学习并为其做出贡献。这推动了累积文化进化的进程,而这一进程造就了人类最卓越的成就。人工智能对社会交互智能体的研究大多关注多智能体环境中的文化(通常在发展心理学方面缺乏坚实的基础)。我们认为,人工智能研究应以心理学为依据,研究那些同样能够融入一种文化的社会认知能力。我们从迈克尔·托马塞洛和杰罗姆·布鲁纳的研究中汲取灵感,他们研究了社会认知发展,并强调文化环境对智力的影响。我们概述了一组比当前人工智能研究中所涉及的更为广泛的概念,以为人工社会智能的研究提供基础。这些概念包括社会认知(共同注意、观点采择)、沟通、社会学习、形式和支架。为了促进该领域的研究,我们推出了SocialAI学派——一种工具,它提供了一套可定制的、通过程序生成的环境参数集。这个工具简化了对所引入概念的实验。此外,这些环境既可以与多模态强化学习智能体一起使用,也可以与作为交互智能体的纯文本大语言模型一起使用。通过一系列案例研究,我们展示了SocialAI学派在研究基于强化学习和大语言模型的智能体方面的多功能性。我们的动机是让人工智能社区围绕受发展心理学启发的社会智能展开研究,并提供一个用户友好的资源和工具,用于朝着这个方向进行初步研究。有关代码和其他资源,请参考项目网站:https://sites.google.com/view/socialai-school 。