Dr. David Sudit Profile

Dr. David Sudit

at Barnstorm Research Corp

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Proceedings Article | 12 April 2021 Presentation

Improving coordination between teams of autonomous agents using TReVe

David Sudit, Wendy Mungovan, John Minahan

Proceedings Volume 11748, 117480H (2021) https://doi.org/10.1117/12.2585809

KEYWORDS: Situational awareness sensors

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Distributed battle management of a group of autonomous agents e.g., unmanned air systems (UAS) facing a highly capable adversary, requires controlling a group of semi-isolated small teams, operating under extensive uncertainty about the enemy situation and also about the status and plans of some of its own agents. Barnstorm Research developed Trust, Refrain and Veto (TReVe), which allows agents to maintain higher situational awareness of teammates when in a denied communications environment. Barnstorm Research has shown that TReVe increases mission success via simulation when onboard RAIDER/FACE-enabled UAS in a denied communications environment.

Proceedings Article | 27 May 2015 Paper

Combining human and machine processes (CHAMP)

Moises Sudit, David Sudit, Michael Hirsch

Proceedings Volume 9499, 94990M (2015) https://doi.org/10.1117/12.2180272

KEYWORDS: Data modeling, Sensors, Mathematical modeling, Reliability, Situational awareness sensors, Strategic intelligence, Cognitive modeling, Blood pressure, Intelligence systems, Image information entropy

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Machine Reasoning and Intelligence is usually done in a vacuum, without consultation of the ultimate decision-maker. The late consideration of the human cognitive process causes some major problems in the use of automated systems to provide reliable and actionable information that users can trust and depend to make the best Course-of-Action (COA). On the other hand, if automated systems are created exclusively based on human cognition, then there is a danger of developing systems that don’t push the barrier of technology and are mainly done for the comfort level of selected subject matter experts (SMEs). Our approach to combining human and machine processes (CHAMP) is based on the notion of developing optimal strategies for where, when, how, and which human intelligence should be injected within a machine reasoning and intelligence process. This combination is based on the criteria of improving the quality of the output of the automated process while maintaining the required computational efficiency for a COA to be actuated in timely fashion. This research addresses the following problem areas:

Providing consistency within a mission:

Supporting the incorporation of data that is uncertain, incomplete, imprecise and contradictory (UIIC):

Developing systems that include humans in the loop whose performance can be analyzed and understood to provide feedback to the sensors.

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