Human-Artificial Interaction in the Age of Agentic AI: A System-Theoretical Approach

TL;DR

The paper addresses the challenge of modeling human–artificial interaction as a dynamic, networked ecosystem comprising heterogeneous agents with autonomous or tightly integrated capabilities. It introduces a formal framework based on colored Petri nets and the concept of communication spaces to unify multi-agent systems and Centaurian architectures, validated through two use cases: satellite/swarm robotics and Large Action Models with neuro-symbolic integration. The contributions include a formal representation that preserves agent boundaries where needed while enabling deep cognitive fusion, a practical implementation blueprint, and concrete demonstrations of MAS and Centaurian collaboration in real-world-like scenarios. The work advances hybrid intelligence by enabling structured coordination with emergent behavior, supporting adaptable, transparent, and scalable human–AI collaboration in autonomous robotics, decision-making, and cognitive architectures.

Abstract

This paper presents a novel perspective on human-computer interaction (HCI), framing it as a dynamic interplay between human and computational agents within a networked system. Going beyond traditional interface-based approaches, we emphasize the importance of coordination and communication among heterogeneous agents with different capabilities, roles, and goals. A key distinction is made between multi-agent systems (MAS) and Centaurian systems, which represent two different paradigms of human-AI collaboration. MAS maintain agent autonomy, with structured protocols enabling cooperation, while Centaurian systems deeply integrate human and AI capabilities, creating unified decision-making entities. To formalize these interactions, we introduce a framework for communication spaces, structured into surface, observation, and computation layers, ensuring seamless integration between MAS and Centaurian architectures, where colored Petri nets effectively represent structured Centaurian systems and high-level reconfigurable networks address the dynamic nature of MAS. Our research has practical applications in autonomous robotics, human-in-the-loop decision making, and AI-driven cognitive architectures, and provides a foundation for next-generation hybrid intelligence systems that balance structured coordination with emergent behavior.

Figures (10)

• Figure 1: A simple Petri net example showing two places and one transition.
• Figure 2: A colored Petri net where tokens carry types or data, allowing conditional transitions.
• Figure 3: Conceptual illustration of a human operator, an LLM agent, and a swarm of drones, each using distinct communication channels (text-based, structured data, and video feed).
• Figure 4: A layered architecture for HCI.
• Figure 5: Schematization of communication spaces in the proposed architecture.