Visualizations for an Explainable Planning Agent

In this demonstration, 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 crucial for establishing human trust and common ground with an 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 demonstrate these functionalities in the context of a smart assistant in the Cognitive Environments Laboratory at IBM's T.J. Watson Research Center.

Towards an Adaptive Question Answering System for Intelligence Analysts

Battelle is working in partnership with Stanford University's Knowledge Systems Laboratory (KSL) and IBM's T.J. Watson Research Center to develop a suite of technologies for knowledge discovery, knowledge extraction, knowledge representation, automated reasoning, and human information interaction, in unison entitled “Knowledge Associates for Novel Intelligence” (KANI). We have developed an integrated analytic environment composed of a collection of analyst associates, software components that aid the analyst at different stages of the analytical process. In this paper, we discuss our efforts in the research, design and implementation of the question answering elements of the Information Interaction Associate. Specifically, we focus on the techniques employed to produce an effective user interface to these elements. In addition, we touch upon the methodologies we intend to use to empirically evaluate our approach with active intelligence analysts.