Should, Want, Can, Will, Do and Be Accountable: Human-Machine and Human-AI Patterns for Integrating the Real World, Virtual Models and Society by Shared Control and Cooperative Systems

Machines, e.g. empowered by AI and based on virtual models, can help to improve the quality of life. To exploit this potential and also integrate this with the real world and society, cooperation and teaming of these machines with humans, and with societies is crucial. Human-Machine Patterns can be a key concept to analyze, understand, design, engineer and evaluate the delicate interplay of humans and machines. Key issues here are to understand in which situations which agents should do, want to do, can do, will do and finally actually do which actions, and who then is accountable. This overview article is intended as an introduction into the special session on Shared and Cooperative Control, especially on patterns and models for controllability and resilience. It is a direct follow-up on the 2022's special session and overview article (which is also available on IEEE Xplore). It introduces the topic of shared and cooperative control of human-machine and human-AI systems, especially in the light of the new advances in AI technology. This paper gives a short overview on the state of research on interaction patterns, controllability and resilience, before it focuses on the fundamental aspects of which actor should do, wants to do, can do, does and will be accountable for a pattern, sub-pattern or action within a pattern. Examples of what can be achieved with this basic architecture are given, e.g. for the recognition of intent or for the support by assistant systems, using the automotive domain as a first application example.