Military personnel will continue to partner with autonomous systems with increasing sophistication and complexity. The training of personnel in tandem with autonomous system model training would provide opportunities to explore human-system co-learning dynamics. This could enable the development of a system that can become an autonomous study partner that could help the trainee acquire new knowledge, learn the strengths and weaknesses of the trainee, and could ultimately shorten the trainees time to proficiency. This co-learning approach may also serve as a mechanism to build trust between the trainee and the system. The opportunity exists to develop and persist Artificial Intelligence and Machine Learning (AI/ML) agents within DoD cloud infrastructure that can follow and assist a service member in training and as a job aid in their initial assignments. CHI Systems has previously developed three technologies that can be leveraged in this pursuit. First, the Narrative Based Reasoning Engine (NBR) developed for the Air Force Research Laboratory provides an executable cognitive architecture that represents knowledge and context as narrative or story elements. Second, the Integrated Context Engine (ICE) is a cognitively inspired whiteboarding system for knowledge representation/data fusion and an orchestration tool that allows the NBR to connect to other systems. Third, the Recognition of Errors and Validation of Input for Self-healing Entry (REVISE) system is a ML-based and context-aware tool that can be dynamically trained to identify errors and recommend corrections in Naval aircraft and shipboard maintenance documentation systems. The team of CHI Systems and our partner Quantum Improvement Consulting propose to develop a Training Associate for Maintenance Informatics (TAMI) system. TAMI will be capable of interacting and partnering with the trainee in a naturalistic way in training and work contexts. TAMI will learn in an evolutionary fashion with the trainee and provide a study partner capability. Through interactions and built-in assessments, TAMI could begin to understand the strengths and weaknesses in the trainees knowledge. We feel that TAMI can ultimately follow a trainee through primary training and follow a servicemember during their apprenticeship.
