In today's rapidly evolving landscape of generative artificial intelligence (AI), Human-Artificial Interaction is becoming increasingly complex and multifaceted. Advanced AI technologies—such as Large Language Models (LLMs) like GPT, AI agents capable of performing complex actions (e.g. agents like Rabbit), and Text-to-Image (TTI) platforms like DALL-E—are reshaping traditional frameworks of Human-Computer Interaction (HCI). These technologies present unique opportunities and challenges, enabling sophisticated computational agents to interact with human users and other artificial agents within interconnected systems.

There is a growing need to understand these interactions' systemic and multi-agent dimensions. This involves focusing on dynamics at both the user interface level and the internal communication and cooperation mechanisms among agents. Despite significant advancements, gaps remain in our understanding of the protocols and procedures that govern these interactions and how emergent behaviors can be leveraged to improve collaboration between humans and AI agents.

This research topic explores the complex dynamics of human-artificial agent interactions in the age of generative AI. By adopting a multi-agent perspective, we seek to uncover how these interactions can be optimized to enhance overall system performance and user experience. This includes investigating how diverse agents—with varying capabilities, roles, and goals—engage in continuous coordination and communication processes. We focus on identifying potential emergent behaviors and developing protocols and mechanisms to improve collaboration and efficiency in multi-agent systems.

We welcome articles that provide insights into human-artificial interaction's systemic and multi-agent aspects. Topics of interest include, but are not limited to:

• Theoretical frameworks and models for multi-agent HCI systems

• Case studies on integrating advanced AI technologies like LLMs, action-oriented AI agents, and TTI platforms into HCI

• Protocols and mechanisms for communication and coordination among agents

• Emergent behaviors in multi-agent systems and their implications

• Effectiveness of human-in-the-loop methodologies, where human input remains central to AI processes

• Practical applications and challenges in implementing multi-agent HCI systems in real-world scenarios.

We are interested in various manuscript types, including original research articles, review articles, theoretical papers, and case studies. Contributions should offer new insights into the systemic interactions between human and artificial agents and propose practical solutions or frameworks applicable in real-world settings.

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Frontiers in Human Dynamics

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