The focus of the Phase 2 effort is to expand the Intelligent Machine Learning (IML) approach taken in Phase 1 to develop a Human-Centered Intelligent Virtual Agent (IVA). IML is an approach that moves the development of machine learning models away from engineers and puts the development of the model in the hands of the end-user. A Human-Centered IVA is focused on continuously improving the Machine Learning (ML) models while also providing effective communication between the human crewmembers and the IVA. Human-centered IVA is a perspective on artificial intelligence and ML that algorithms must be designed with awareness of being part of a larger system that includes end-users. This approach allows the IVA to incorporate the knowledge, insight, and feedback of the end-users allowing for tuning and refinement of the ML models. The Human-centered IVA will assist crewmembers in various tasks e.g., crew scheduling, procedure creation, and anomaly detection and resolution during a long-duration mission. ADAPT's Human-Centered IVA will provide the computationally heavy-lifting while still receiving inputs and insights from the crewmembers. This allows for the expansion of processes and information to a larger scale without compromising data integrity or mission success due to a lack of ground assistance.
To provide crewmembers with a Human-Centered IVA this effort leverages the supervised learning algorithms Decision Tree, Random Forest, Ada Boost, Gradient Boost, Extreme Gradient Boost, Categorical Boost, and Associative Rule Models which were shown in Phase I to be succesful within an IML approach. Additionally, this phase will focus on providing an explainable interface that allows the end-user to query the IVA for the reason behind its prediction. This will be accomplished using an interactive visualization Graphical User Interface.
We expect the Human-centered Intelligent Virtual Agents (IVA) approach to improving model predictions throughout all phases of a long-duration mission will be of interest to several groups within NASA. The ARTEMIS program for example could make use of IVAs to assist the crew in similar scenarios used during the development. Additionally, this work will be of interest to the EVA Exploration Office, the EVA Strategic Planning and Architecture group, and the Exploration Mission Planning Office.
The proposed cognitive architecture will benefit several TRACLabs commercial customers. We expect the ability of end-users to direct the adaptation of the system will be of interest. For example, Baker Hughes has already expressed interest in licensing some of the new capabilities being developed in previous cognitive agent efforts, particularly the ontology and anomaly management aspects.