Foundational concepts in person-machine teaming - Greenberg, Ariel M.; Marble, Julie L. (Frontiers in Physics, 2023)
As we enter an age where the behavior and capabilities of artificial intelligence and autonomous system technologies become more complex, collaboration, teaming and cooperation between people and these machines is rising to the forefront of critical research areas. People are inherently social in their interactions not only with other people, but also with non-human entities such as, dogs, horses, and even machines. However, machines are not social, nor do they have affect. Experiential robotics identifies the need to develop machines that not only learn from their own experience, but can learn from the experience of people in interactions, wherein these experiences are primarily social. In this paper, we argue, therefore, the need to place experiential considerations in teaming, cooperation, and interactions as the basis for design and engineering of people-machine teams. We explore the importance of semantics in driving engineering approaches to robot development, and then explore differences in these terms between engineering and social science approaches and the implication for the development of autonomous, experiential systems, with respect to trust, ethics, and vulnerability.