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In this paper, we report on the visualization capabilities of an ExplainableAI Planning (XAIP) agent that can support human in the loop decision making.Imposing transparency and explainability requirements on such agents isespecially important in order to establish trust and common ground with theend-to-end automated planning system. Visualizing the agent's internaldecision-making processes is a crucial step towards achieving this. This mayinclude externalizing the "brain" of the agent -- starting from its sensoryinputs, to progressively higher order decisions made by it in order to driveits planning components. We also show how the planner can bootstrap on thelatest techniques in explainable planning to cast plan visualization as a planexplanation problem, and thus provide concise model-based visualization of itsplans. We demonstrate these functionalities in the context of the automatedplanning components of a smart assistant in an instrumented meeting space.

Visualizations for an Explainable Planning Agent