Human-Artificial Interaction in the Age of Agentic AI: A System-Theoretical Approach
It addresses the challenge of designing effective human-AI collaboration systems for applications like autonomous robotics and decision-making, though it appears incremental by building on existing system-theoretical concepts.
This paper tackles the problem of human-computer interaction by framing it as a dynamic interplay between human and computational agents, introducing a framework for communication spaces to formalize interactions between multi-agent systems and Centaurian systems.
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.