Future space missions will rely heavily on automated multi-agent cyber-physical-human teams to perform a number of tasks, such as robotic servicing, habitat maintenance, health management, etc. In order for these multi-agents to become a reality, trust in them and uncertainty quantification will need to be factored into decision making policies by the system participants. Even if one assumes perfect information the problem is NP-Hard, thus timely optimal solutions are unachievable. The central objective of the proposed technology is to provide near optimal mission planning for autonomous multi-agents with uncertain and possibly untrustworthy data sources using a hybrid deep reinforcement learning-optimization approach. A new architecture will be developed under this optimization approach that will allow for consideration of uncertainty and trustworthiness in planning decisions. A use-case that incorporates realistic uncertainties will be employed to provide metrics on the proposed approach. The use-case involves multiple satellites working in coordination to provide vital information to ground agents, each with a task of resupplying outlying bases. Past results by the investigators provide a basis-for-optimism that the proposed approach is viable. The Phase I effort will focus on extensive simulation studies and analyses. This will build a foundation to develop benchmark testing at the onset of Phase II, with the end of this work being a fully functional demonstration unit. Potential NASA Applications (Limit 1500 characters, approximately 150 words): NASAs Cyber-Physical Systems Modeling and Analysis Initiative was developed to support future space exploration missions. Autonomous multi-agent cyber-physical-human teams will be a vital aspect of this initiative. Past realizations to minimize the impact of costly validation and verification (V&V) processes resulted in minimally scoped autonomous operations. Current V&V processes for autonomous space operations will clearly require a much high level of trustworthiness than ever before, which will be provided by the proposed technology. Potential Non-NASA Applications (Limit 1500 characters, approximately 150 words): An obvious example involves the intelligent manufacturing sector, which integrates information technology and manufacturing technology. A large sector that has led to a disruptive impact is the driverless automobile sector. Both sectors will rely heavily on multi-agent cyber-physical-human teams. The proposed technology is easily extendable to these and other non-NASA sector applications.
