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. The paper distinguishes between Multi-Agent Systems (MAS)—where agents maintain autonomy through structured cooperation—and Centaurian systems, which integrate human and AI capabilities for unified decision making. 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. We recognize that elements such as task recommendation, feedback loops, and natural language interfaces are common in contemporary adaptive HCI. What distinguishes our framework is not the introduction of these elements per se, but the synthesis of architectural principles that systematically accommodate both autonomy-preserving and integration-seeking configurations within a shared formal foundation. 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.

Serious games receive increasing interest in the area of e-learning. Their development, however, is often still a demanding, specialized and arduous process, especially when regarding reasonable non-player character behaviour. Reinforcement learning and, since recently, also deep reinforcement learning have proven to automatically generate successful AI behaviour to a certain degree. These methods are computationally expensive and hardly scalable to various complex serious game scenarios. For this reason, we introduce a new approach of augmenting the application of deep reinforcement learning methods by interactively making use of domain experts' knowledge to guide the learning process. Thereby, we aim to create a synergistic combination of experts and emergent cognitive systems. We call this approach interactive deep reinforcement learning and point out important aspects regarding realization within a framework.

Serious games belong to the most important future e-learning trends and are frequently used in recruitment and training. Their development, however, is still a demanding and tedious process, especially when regarding reasonable non-player character behaviour. Serious games can generally profit from diverse, adaptive behaviour to increase learning effectiveness. Deep reinforcement learning has already shown considerable results in automatically generating successful AI behaviour, but its past applications were mainly focused on optimization and short-horizon games. To expand the underlying ideas to serious games, we introduce a new approach of augmenting the application of deep reinforcement learning methods by interactively making use of domain experts’ knowledge to guide the learning process. Thereby, we aim to establish a synergistic combination of experts and emergent cognitive systems to create adaptive and more human behaviour. We call this approach interactive deep reinforcement learning and point out important aspects regarding realization within a novel framework.

Purpose: Recent advances in AI, especially in large language models, have created new opportunities to integrate human and artificial agents through shared linguistic capabilities. This paper presents a multi-agent organizational framework in which human agents, LLMs, and specialized agents (narrow AIs) collaborate via dynamic, topic-based group formation. Topic-driven interactions enable agents to coalesce around evolving interests, supported by threshold-based protocols for temporal adaptation, topic emergence, and participation. Methods: Within our framework, human agents guide the overall system objectives, while consultant agents (LLMs) provide semantic analysis and mediation, and specialized agents perform focused domain tasks. By leveraging automated topic modeling, the approach eschews rigid ontologies and instead supports adaptive and interpretable content management. Mathematical properties ensure system coherence—across roles, tasks, and timescales—while allowing the natural evolution of interests and groups. Results: We illustrate the framework’s versatility with example scenarios in emergency response, healthcare research, and financial decision-making, emphasizing how human decision-makers, LLM-based consultants, and specialized worker agents jointly fulfill complex goals through transparent topic alignment and threshold-driven coordination. This formalization advances human-computer interaction as a multi-agent phenomenon that integrates human insight with the strengths of next-generation AI models in a cohesive, evolving system.