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

TL;DR

This paper introduces a system-theoretical framework for human-AI interaction, emphasizing coordination among heterogeneous agents and distinguishing between multi-agent and Centaurian systems for advanced hybrid intelligence.

Contribution

It formalizes communication spaces and models for human-AI collaboration, bridging MAS and Centaurian paradigms with structured interaction frameworks.

Findings

Structured communication layers enable seamless integration.

Colored Petri nets effectively model Centaurian systems.

Reconfigurable networks support dynamic multi-agent systems.

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.