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 <i>emergence</i> 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 <i>enter</i> 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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Publication Details

Journal
Frontiers in Neurorobotics
Published
2024-10-09
DOI
https://doi.org/10.3389/fnbot.2024.1396359
Citations
7
Primary Topic
Language and cultural evolution
Type
article
Field-Weighted Citation Impact
30.03

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article

The SocialAI school: a framework leveraging developmental psychology toward artificial socio-cultural agents

Grgur Kovač, Rémy Portelas, Peter Ford Dominey, Pierre-Yves Oudeyer
7 citations
Frontiers in Neurorobotics
Language and cultural evolution
30.03
article

The SocialAI school: a framework leveraging developmental psychology toward artificial socio-cultural agents

Grgur Kovač, Rémy Portelas, Peter Ford Dominey, Pierre-Yves Oudeyer
article en
7 citations

Abstract

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 <i>emergence</i> 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 <i>enter</i> 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.

Frontiers in NeuroroboticsVol. 18
Institut national de recherche en sciences et technologies du numérique (FR), Inserm (FR), Université de Bourgogne (FR), Ubisoft (France) (FR)
Agence Nationale de la Recherche, Grand Équipement National De Calcul Intensif
Quality Education
Openalex Percentile: Top 0%
Language and cultural evolution
30.03
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