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Human-AI collaboration in Hybrid Multi-Agent Systems

Labedzki Rafal

International Journal of Electronics and Telecommunications

2025

Vol. 71, No. 4

This paper examines Hybrid Multi-Agent Systems, integrating both human and non-human intelligent agents, as a new subject of management research. It presents original definitions of key concepts: intelligent agents, artificial intelligent agents, and Hybrid Multi-Agent Systems. These definitions are grounded in Distributed Artificial Intelligence and provide a foundation for exploring the collaboration between human and artificial intelligent agents. The study addresses fundamental research questions regarding the nature of intelligent agents and their role within Multi-Agent Systems, proposing Hybrid Multi-Agent Systems as a novel framework that allows for seamless cooperation between human and non-human entities. Through a narrative literature review, this paper highlights the potential implications of Hybrid Multi-Agent Systems for scientific research in management, offering a conceptual basis for future research in this evolving field.

Trust-building in AI-human partnerships within Industry 5.0

Żywiołek Justyna

System Safety : Human - Technical Facility - Environment

2024

Vol. 6, iss. 1

89--98

The rapid advancement of artificial intelligence (AI) within Industry 4.0 has transformed manufacturing processes, shifting from traditional automation to more collaborative AI-human partnerships. While AI promises enhanced efficiency, precision, and productivity, the success of these systems relies heavily on the trust established between human operators and AI technologies. This paper explores the critical factors influencing trust in AI-human partnerships in the manufacturing sector, emphasizing the need for transparency, accountability, and ethical AI design. Drawing on a multidisciplinary literature review and empirical studies, we identify key drivers of trust, including human preferences for system transparency, the explainability of AI decisions, and the reliability of AI systems in dynamic production environments. Furthermore, the paper examines the challenges associated with trust-building, such as overcoming fear of job displacement and managing perceived risks of AI errors. The findings contribute to the growing body of knowledge on human-centric AI design and offer practical recommendations for fostering trust to ensure successful AI-human collaboration in manufacturing settings. By transitioning from purely automated systems to collaborative partnerships, manufacturers can unlock the full potential of AI while maintaining a workforce that is confident in AI’s reliability and ethical alignment.