SocialAI 学校:利用发展心理学实现人工社会文化代理的框架。

IF 2.6 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Frontiers in Neurorobotics Pub Date : 2024-10-09 eCollection Date: 2024-01-01 DOI:10.3389/fnbot.2024.1396359
Grgur Kovač, Rémy Portelas, Peter Ford Dominey, Pierre-Yves Oudeyer
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引用次数: 0

摘要

长期以来,发展心理学家一直认为社会认知能力是人类智力和发展的基础。这些能力使个人能够进入周围的文化,从中学习并做出贡献。这推动了文化的累积进化过程,而人类最杰出的成就正是由这一过程促成的。有关社会互动代理的人工智能研究大多涉及多代理环境中文化的出现(通常没有发展心理学的坚实基础)。我们认为,人工智能研究应借鉴心理学知识,研究进入文化的社会认知能力。我们从迈克尔-托马塞罗(Michael Tomasello)和杰罗姆-布鲁纳(Jerome Bruner)的研究中汲取灵感,他们研究社会认知发展,强调文化环境对智力的影响。我们概述了一套比目前人工智能研究更广泛的概念,为人工社会智能的研究奠定了基础。这些概念包括社会认知(共同注意、视角把握)、交流、社会学习、格式和支架。为了促进这一领域的研究,我们推出了 SocialAI 学校--一种提供可定制参数化程序生成环境套件的工具。该工具简化了对所引入概念的实验。此外,这些环境既可用于多模态 RL 代理,也可用于纯文本大语言模型(LLM)交互代理。通过一系列案例研究,我们展示了 SocialAI 学校在研究基于 RL 和 LLM 的代理方面的多功能性。我们的动机是让人工智能社区参与到以发展心理学为基础的社会智能中来,并为这一方向的初步研究提供用户友好型资源和工具。有关代码和其他资源,请访问项目网站:https://sites.google.com/view/socialai-school。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The SocialAI school: a framework leveraging developmental psychology toward artificial socio-cultural agents.

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 emergence 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 enter 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.

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来源期刊
Frontiers in Neurorobotics
Frontiers in Neurorobotics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCER-ROBOTICS
CiteScore
5.20
自引率
6.50%
发文量
250
审稿时长
14 weeks
期刊介绍: Frontiers in Neurorobotics publishes rigorously peer-reviewed research in the science and technology of embodied autonomous neural systems. Specialty Chief Editors Alois C. Knoll and Florian Röhrbein at the Technische Universität München are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neural systems include brain-inspired algorithms (e.g. connectionist networks), computational models of biological neural networks (e.g. artificial spiking neural nets, large-scale simulations of neural microcircuits) and actual biological systems (e.g. in vivo and in vitro neural nets). The focus of the journal is the embodiment of such neural systems in artificial software and hardware devices, machines, robots or any other form of physical actuation. This also includes prosthetic devices, brain machine interfaces, wearable systems, micro-machines, furniture, home appliances, as well as systems for managing micro and macro infrastructures. Frontiers in Neurorobotics also aims to publish radically new tools and methods to study plasticity and development of autonomous self-learning systems that are capable of acquiring knowledge in an open-ended manner. Models complemented with experimental studies revealing self-organizing principles of embodied neural systems are welcome. Our journal also publishes on the micro and macro engineering and mechatronics of robotic devices driven by neural systems, as well as studies on the impact that such systems will have on our daily life.
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