Visualizations for an Explainable Planning Agent
This work addresses the need for explainability in AI planning systems to support human-in-the-loop decision-making, but it appears incremental as it builds on existing explainable planning techniques.
The paper tackles the problem of making AI planning agents more transparent and trustworthy by developing visualization capabilities that externalize the agent's internal decision-making processes, from sensory inputs to higher-order decisions, and demonstrates these functionalities in a smart assistant for an instrumented meeting space.
In this paper, we report on the visualization capabilities of an Explainable AI Planning (XAIP) agent that can support human in the loop decision making. Imposing transparency and explainability requirements on such agents is especially important in order to establish trust and common ground with the end-to-end automated planning system. Visualizing the agent's internal decision-making processes is a crucial step towards achieving this. This may include externalizing the "brain" of the agent -- starting from its sensory inputs, to progressively higher order decisions made by it in order to drive its planning components. We also show how the planner can bootstrap on the latest techniques in explainable planning to cast plan visualization as a plan explanation problem, and thus provide concise model-based visualization of its plans. We demonstrate these functionalities in the context of the automated planning components of a smart assistant in an instrumented meeting space.