TRACS -- A Cognitive Architecture for Deep Space Missions

Future deep space missions will require artificial cognitive agents are able to interface with onboard systems and take over time-consuming routine tasks, thus reducing crew cognitive and work load. In addition, they should continuously monitor critical onboard systems and alert crew about any off-nominal operation, quickly responding to them according to predetermined procedures in cases where the crew is overloaded or would be endangered.nbsp;We propose our fully implemented Thinking Robots Autonomous Cognitive System (TRACS) architecture at TRL5 as the basis for a cognitive agent for future NASA deep space missions.nbsp; TRACS is open, modular, makes decisions under uncertainty, and learns in a manner that the performance of the system is assured and improves over time.nbsp; It deeply integrates natural language capabilities and one-shot learning from instructions, observations, and demonstrations.nbsp; TRACS allows for easynbsp;integration with commercial off-the-shelf components and third-party modules, components, and software libraries, and has extensive integrated fault detection, fault exploration, and recovery methods.nbsp; TRACS has also been successfully used in several projects with NASA collaborators at NASA Langley and NASA Ames.nbsp; In this project,nbsp;(1) the fully implemented and operational interactive cognitive TRACS architecture, extended by episodic memory for long-term interactions andnbsp;additionalnbsp;annotation mechanisms for facilitating assurance,nbsp;together (2) with the results from a feasibility study in NASA-funded simulation environment demonstrating the full operation of the architecture in interactive human-subject experiments, and (3) a detailed plan for the application domains, system integration, and evaluations in Phase II based on NASA objectives to be developed in collaboration with NASA.