ADAPtive agenT Architecture

We are proposing a cognitive agent architecture that leverages Interactive Machine Learning, which will allow ground controllers and crewmembers the ability to train and retrain their cognitive agents during a mission based on new/novel mission data. Interactive Machine Learning (IML) is a human-centered paradigm in which end-users, e.g. crewmembers and ground-based Subject Matter Experts, iteratively build and refine the ML model through iterative cycles of input and review. Model refinement is driven by user input that may come in many forms, such as onboard data and communications, crew preferences, modified mission parameters along with a description of features and selection of high-level model parameters. IML is distinct from classical machine learning in that human intelligence is applied through iterative teaching and model refinement in a relatively tight loop. With this approach, our goal is to develop an architecture that provides end-users with the ability to interactively explore and adapt the training space with the goal of guiding the adaptation of the cognitive agent toward an intended behavior. This approach will allow crewmembers the ability to control how the cognitive agent learns and adapts during a long duration exploration mission, assuring that its performance improves and does not degrade over time. This work will present crewmembers and SME centered approach to applying IML methods to the design of a system that learns and adapts based on crewmembers inputs to the system.nbsp;By leveraging IML we are able to address one of the primary challenges associated with the use of cognitive agents during a long-duration mission, the inability to adapt and learn from observation, instruction and interaction as missions proceed.