AI-AUGMENTED DECISION SUPPORT SYSTEMS: APPLICATION IN MARITIME DECISION MAKING UNDER CONDITIONS OF METOC UNCERTAINTY

The ability for a human to overlay information from disparate sensor systems or remote databases into a common operational picture can enhance rapid decision making and implementation in a complex environment. This thesis focuses on operational uncertainty as a function of meteorological and oceanographic (METOC) effects on maritime route planning. Using an existing decision support system (DSS) with artificial intelligence (AI) algorithms developed by New Jersey Institute of Technology and University of Connecticut, cognitive load and time to decision were assessed for users of an AI-augmented DSS, accounting for METOC conditions and their effects, and users of a baseline, 'as is,' DSS system. Scenario uncertainty for the user was presented in the relative number of Pareto-optimal routes from two locations. Key results were (a) users of an AI-augmented DSS with a simplified interface completed assigned tasks in significantly less time than users of an information-dense, complex-interface AI-augmented DSS; (b) users of simplified, AI-augmented DSS arrived at decisions with lower cognitive load than baseline DSS and complex-interface AI-augmented DSS users; and (c) users relied mainly on quantitative data presented in tabular form to make route decisions. The differences found in user performance and cognitive load between levels of AI augmentation and interface complexity serve as a starting point for further exploration into maximizing the potential of human-machine teaming.Office of Naval ResearchMajor, United States Marine CorpsApproved for public release. distribution is unlimite