Human-Machine Situation Analysis by Design

For many years, there has been an interest to support human situation awareness through data automation tools and information fusion systems design that perform situation assessment. With large data ingestion and computing power support, machines can provide historical narrative presentations, information fusion estimation, and plausible future prediction. The tools and techniques are of interest within human-machine teaming (HMT) to combine user reasoning with analytical agents. Deployable HMT solutions require user evaluations, which should be conducted throughout the system design life cycle. Likewise, to determine which design solutions meet the user needs, a preliminary scaling is required to rank candidate solutions. This paper presents a notional approach for human-agent designs to support situational analysis evaluation highlighting user value selection through analytical hierarchy processing (AHP) of metrics developed from the Multisource AI Scorecard Table (MAST). The MAST AHP technique helps to rank situation analysis tools in development design, while the paper highlights one example from the Association of Chats to Tracks (ACT) that facilitates cognitive reasoning of situational scenarios.