The Plan Future Graph Visualization (PFG-Viz) project has been examining Explainable AI (XAI) and Human-AI Teaming (HAT) techniques which support the human planner. In the context of the creation of a set of plausible plans generated from an Artificial Intelligence (AI) planning system, these techniques support the goal to provide AI generated plans which are to be understood, assessed, modified, and approved by the human planner. The evaluation of the AI generated plans is built on a model and used to formulate a user interface. Phase One provided a hand-crafted baseline use case and scenario used to understand and constrain the variables and support an initial model for implementation and validation. A logistics-based land delivery scenario looks at a constrained set of routes from the departure point to delivery destinations. Metrics applied were distance, time, risk, and delivery point. The metrics were quantized for each plan trajectory. The model was based on a tree graph structure specifically designed for the use case, selected metrics, and consideration for scope and complexity. Phase Two is a continuation of examining the validation of the approach through use of digital twin simulation with an embedded Goal-Oriented Action Planning (GOAP) AI algorithm. Instrumentation in the digital twin is used within the synthetic environment for data collection. Phase Two will also include a model of human interaction with the designed user interface using an application called IMPRINT. This will examine how the human planner can more effectively interact with the output of the AI planning system.