Human Machine Teaming for Reduction of Operator Cognitive Load

US Special Operations Command (SOCOM) is increasingly reliant on technology to underpin critical operations integral to our national security and to compete against great powers on the technological field of battle. As adversaries, including peer competitors, invest and operationalize technology to degrade Special Operations Force’s (SOF) mission performance, USSOCOM must make corresponding investments in technology that hyper-enable Special Operators. To do this, technology must be agnostic, flexible, and scalable to incorporate advances across a range of disciplines. The field of artificial intelligence (AI), and specifically machine learning, computer vision, and decision support systems, can greatly assist companies and organizations with human machine teaming (HMT), which is “is a relationship - one made up of at least three equally important elements: the human, the machine, and the interactions and interdependencies between them.” One of the most critical reasons to use HMT-related technologies is to offload cognitive load from a human operator or user onto the computer or technology. This allows the computer to quickly accomplish tasks better suited for it, such as AI-related tasks, repetitive and time-consuming tasks, or decision support. A decision support system (DSS) is a combination of many concepts addressed above. A DSS is a “model-based set of procedures for processing data and judgments to assist decision makers to solve semi-structured and unstructured decision tasks.” An AI-DSS is a DSS that makes use of AI techniques, such as expert systems, bayesian networks, fuzzy logic, and neural networks to allow processing of massive amounts of data available, making this information available as digestible knowledge for human decision makers to make better informed decisions at any level. An AI-DSS is critical because it offloads tasks previously being performed by humans to the AI-DSS, allowing the user’s observe, orient, decide, and act (OODA) loop to cycle faster due to effective HMT pairing. It is increasingly difficult to both analyze and interpret vast amounts of data into a digestible, human-understandable format to enable decision making. Additionally, the current state of AI and Decision Support Systems makes it possible to offload some of this operator cognitive load onto machines to assist in the processing, analyzing, and display of relevant information and knowledge, allowing humans additional cognitive capacity to focus on other tasks requiring human attention. To achieve these goals, SOCOM seeks to develop a SOCOM AI Decision Assistance Robot (SAIDAR) that is a SOF-unique infrastructure, deployable on the range of platforms relevant to SOCOM - from simple, single-machine deployments on tactical devices and commodity hardware, all the way up to multi-machine, multi-tenant private or commercial cloud deployments - supporting a wide variety of decision support tasks throughout SOCOM.