How do we create artificially intelligent agents capable of meaningful and trusted teaming with humans for training and operations? “Common ground” refers to congruent knowledge, beliefs, and assumptions among a team about their objectives, context, and capabilities. It has been a guiding principle in cognitive systems engineering for human-AI interaction, where research has focused on improving communication between human and machines. Coordination (e.g., directability) and transparency (e.g., observability and predictability) are important for establishing, maintaining, and repairing both human-AI and human-human common ground. Nonetheless, human-AI common ground remains relatively impoverished, and AI remains a tool rather than a teammate. From human to machine, communication sets the state of the machine (coordination), and from machine to human, communication the reveals state of the machine to the human (transparency). Among humans, common ground occurs at the level of concept structure; however, human concepts are not merely variables to be parameterized, but are constructed during discourse. For example, an instructor uses communication to activate and shape concepts (e.g., through dialog) in the student’s mind, contextualizing and refining concepts until shared perceptions are categorized (i.e., understood) in a common way. To increase autonomy and human-AI teaming, the challenge is to provide the AI with human-like conceptual structure. An architecture to enable human-AI common ground must provide the AI with representational capacity and algorithms that mimic features of human conceptual structure and flexibility. Here, we identify critical features of human conceptual structure, including Conceptual Blending, Situated Categorization, and Concept Degeneracy. We describe challenges of implementing these features in AI and we outline technical approaches for hybrid symbolic/subsymbolic AI to meet those challenges. As contemporary human-factors approaches to human-AI common ground continue to mature, common ground issues will move from interface transparency to concept congruency.

Keywords
AI;HUMAN FACTORS

Additional Keywords
human-machine teaming, cognitive architecture, common ground