How can AI agents self-organize for complex goals?
Artificial Theory of Mind and Self-Guided Social Organisation
This paper explores how individual agents in a group coordinate their actions to achieve common goals, drawing parallels between biological systems (neurons, ant colonies) and human social structures. It highlights the importance of Theory of Mind (ToM), language, and causal cognition in human social organization and discusses how these concepts relate to the development of multi-agent AI.
For LLM-based multi-agent systems, the key takeaway is the potential for integrating ToM-like capabilities. The paper suggests that current AI, including LLMs, lacks the sophisticated social cognition humans possess, particularly the ability to understand and manipulate social connections to achieve collective goals. This points towards a direction for future research in which LLMs could be enhanced with mechanisms for modeling other agents' mental states, understanding social causality, and dynamically adjusting their interactions within a network. The research emphasizes that such development would necessitate careful consideration of the ethical implications and cautious application of psychological terms within the AI context.