How can LLMs coordinate with human agents in apps?
Human-Artificial Interaction in the Age of Agentic AI: A System-Theoretical Approach
February 21, 2025
https://arxiv.org/pdf/2502.14000This paper proposes a new way to think about human-computer interaction (HCI) by viewing it as a dynamic system of multiple interacting agents, including humans and AI. It distinguishes between two types of interaction: multi-agent systems (MAS), where agents maintain autonomy and interact through protocols, and Centaurian systems, where human and AI capabilities are deeply integrated. The paper introduces "communication spaces" as a framework to model both MAS and Centaurian systems, structured into surface, observation, and computation layers. Colored Petri nets are used to formalize these interactions.
Key points for LLM-based multi-agent systems include:
- LLMs enable humans to act as full-fledged agents, expanding the possibilities for multi-agent interaction.
- LLMs enable deeper human-AI integration in Centaurian systems, facilitating more complex tasks.
- Communication spaces offer a flexible framework for modeling heterogeneous agent interaction, crucial for systems involving LLMs alongside other agent types.
- The "data flywheel" effect (continuous learning and adaptation) applies to both agent improvement in MAS and the refinement of human-AI integration in Centaurian systems, making LLMs a powerful tool for evolving multi-agent systems. This aligns with how communication spaces facilitate flexible interaction.
- Formal modeling with Colored Petri nets provides a rigorous way to design and analyze the complex interactions of LLM-based multi-agent systems, including specifying how LLMs coordinate with other agents.