A Review of AI in Human-Machine Cooperation: Machine Perspective

This paper presents a comprehensive analysis of AI’s role in human-machine cooperation (HMC), offering an integrated perspective on how machine agents assess and interact with humans. While previous research examined individual aspects like human assessment, trust development, or function allocation separately, we integrate these components into a holistic framework for cooperative systems. We examine two assessment approaches: external methods (observing human cognitive states, intentions, and communications) and internal approaches (using cognitive models to emulate human thinking). Applications in manufacturing and autonomous vehicles demonstrate these concepts systematically. Building on these assessments, we investigate how AI enables machines to develop and calibrate trust in human partners, and how it optimizes human-machine interaction through intelligent function allocation and interference management. The paper addresses challenges and future research directions in human assessment, machine trust development, transparency, and interaction optimization. This review provides structured insights for utilizing AI in designing effective HMC systems where machines can assess humans, build appropriate trust, maintain transparency, and interact optimally to enable successful cooperation.