In recent years, tremendous progress has been made in computational intelligence and machine learning methods, which promote the deployment of neural networks and intelligent systems in many living scenarios and industrial sectors. The intelligent agents, including intelligent vehicles and robots, are now part of our lives, and the proportion will become larger. The smartness, safety and efficiency of the interactions and collaboration between humans and intelligent agents should be further improved. To do this, the multimodal human-machine interaction (M-HMI) should be further explored by leveraging advanced machine learning for advancing the co-existence, collaboration, and interactions between humans and intelligent agents.

The information in the real world usually comes in a variety of modalities. Well-designed M-HMI requires the agents to understand the multimodal human performance and unstructured scenario information to develop a mutual cognitive model for effective control, interaction, and collaboration. The challenges in multimodal information fusion, computational modeling and control for M-HMI need to be settled. Meanwhile, based on the mutual understanding between humans and machines, humans will be able to understand and forecast the capabilities and limitations of autonomous agents, and provide the necessary guidance and assistance via M-HMI. Similarly, intelligent machines should also assist humans more efficiently based on their situation awareness and improve human performance in complex tasks.

The aim of this Special Issue is to compile recent research and development efforts contributing to advances in learning-based computational modeling and intelligent algorithms for M-HMI. The Special Issue will also encourage contributions addressing the state-of-the-art in associated tasks, and perspectives on future developments and applications. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Advanced machine learning methods for M-HMI
• Computational modeling of human performance for M-HMI
• Human-machine shared control for M-HMI
• Data-driven perception and sensing for M-HMI
• Multi-modal human-machine interface design for M-HMI
• Multi-modal cooperative decision-making and planning for M-HMI
• Condition monitoring, fault diagnosis and fault-tolerant control for M-HMI
• Multi-modal data safety and security methods for M-HMI
• Intelligent testing and evaluation methods for M-HMI
• Multi-modal human-robot mutual understanding and trustiness for M-HMI
• Other applications of computational modeling and intelligent algorithms for M-HMI