Multi-Criteria Decision Making in Human-Machine Systems:Applications of the WASPAS Method

This study explores the evolution and integration of human-machine interfaces (HMI) across various technological domains. The research examines how advanced computational tools, artificial intelligence, and intelligent decision support systems are revolutionizing human-machine interaction. We analyze multiple aspects of HMI implementation, from simple LED indicators to sophisticated graphical user interfaces, emphasizing their role in facilitating effective communication between humans and automated systems. The study highlights key developments in emotional recognition and regulation within human-machine interactions, including the implementation of empathic virtual conversation agents and speaker identification systems. Particular attention is given to semi-autonomous systems, such as assisted wheelchairs, which balance user control with automated safety features. The research also explores the advancement of embedded information systems in automotive applications, emphasizing the importance of V2X communication-based HMI systems and their hierarchical information management challenges. Furthermore, the study examines the integration of autonomous robots in various settings, from domestic environments to public spaces, and analyzes the implementation of brain-computer interfaces (BCIs) as complementary input devices. The research also investigates decision-making methodologies, particularly The WASPAS (Weighted Integrated Sum Product Assessment) method has proven its effectiveness in addressing multi-criteria decision-making challenges in various applications applications, including supplier selection and system optimization. The findings suggest that successful human-machine interaction requires a careful balance between autonomous functionality and user control, supported by sophisticated emotional intelligence capabilities and efficient decision-support systems. The research concludes that as smart autonomous systems become more prevalent in everyday life, the development of intuitive and emotionally intelligent interfaces becomes increasingly crucial for effective human-machine collaboration. This research adds to the expanding field of human-machine interaction and provides important insights for future developments in this field.