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 ever more sophisticated, cooperation, collaboration, and teaming between people and these machines is rising to the forefront of critical research areas. People engage socially with almost everything with which they interact. However, unlike animals, machines do not share the experiential aspects of sociality. 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, for the need to place experiential considerations in interaction, cooperation, and teaming as the basis of the design and engineering of person-machine teams. We first explore the importance of semantics in driving engineering approaches to robot development. Then, we examine differences in the usage of relevant terms like trust and ethics between engineering and social science approaches to lay out implications for the development of autonomous, experiential systems.