Military operations require fast, decisive, and accurate decision making to accomplish missions with optimal performance and minimization of exposure to risk. Military leaders are forced to make these decisions under constant pressure, changing circumstances, incomplete information, and very short time frames with minimal margin for error. Advancements in Artificial Intelligence (AI) and, specifically, Deep Learning (DL) are helping train computers to accomplish tasks under these same conditions by recognizing patterns across massive data streams. Research in competitive gaming, especially Real-Time Strategy (RTS) games, have shared an interesting symbiosis with the advancement of AI approaches to complex decision-making and hierarchical planning. RTS games are characterized by their imperfect information, large state and action spaces, and necessary balancing of high and low-level planning. The conceptual underpinnings of these game dynamics are highly relevant to problems that are familiar to the military intelligence community, such as knowing how to parameterize plans and when to execute them. Partial observability of environments compounds these difficulties by adding a degree of uncertainty to the mix. However, a question that remains to be answered is how well recent accomplishments in gaming agents, can improve real-world decision-making aids that require situational awareness over high-dimensional observations. DL approaches are making it possible for machines to learn from experiences, adapt to new data, and provide recommendations on optimal behavior. Insights from these models can be communicated to users and analytics providing an intelligence and command advantage. However, the lack of transparency and explainability have made it impossible for these decision makers to put lives at risk based on a black box opinion on the best path forward. In addition, current AI and DL solutions focus on individual decisions based on the current situation with minimal concern for longer term strategic impacts. To solve these two shortcomings, the Machina Cognita Technologies team proposes to develop the State-based Machine Aided Real Time Strategy (SMARTS) engine. The SMARTS engine will provide the ability to analyze an array of potential sequences of actions (or decision tracks), the risks associated with each of these actions, and the required capabilities and effectiveness for units to execute the actions. In addition, the SMARTS engine will provide clear explanations as to the reasoning behind the recommended actions, the impact on mission effectivities, and the possible outcomes for the recommended actions and alternative paths. Specifically, we seek to create a machine learning framework for distilling mission plans into interpretable strategic and tactical decision tracks using a combination of behavioral cloning, unsupervised learning, and attention networks.
Benefit: Transition targets for the SMARTS engine include Naval and Marine Command and Control Programs of Record (e.g. Maritime Tactical Command and Control (MTC2), Gaming and Sports Decision Support Systems (e.g. Riot Games and Uncaged Sports), and Business Consulting services. MCT is currently working with multiple organizations with the primary goal of transitioning existing research and development built by companies as production level products. These products include NLP frameworks in support of enterprise geospatial search and entity management, a sports analytics toolkit focused on improving athletes training practices, and a requirements analysis engine utilizing NLP to ensure standardized approaches to requirement creation. The technologies being developed under this SBIR would provide direct benefit to each of these product lines. Military Command and Control Programs of Record (e.g. MTC2) Summary: Integration of the SMARTS system within the military C2 programs is the overall goal of this effort. The SMARTS system will enable improved decision making through the use of DL technology. Lead: Connections within USN and USMC C2 programs of record. Benefit to the User: Provide Commanders with the best possible decision support with explainable recommendations on actions to be taken. Gaming and Sports Decision Support Summary: Professional and Recreational e-Sports are becoming increasingly popular and lucrative. The SMARTS engine would provide a distinct advantage to e-Sports teams through improved decision making and optimized play. Benefit to the User: Provide real-time feedback for training of e-Sports teams
Keywords: Deep Learning, Deep Learning, Real-time Strategy, Artificial Intelligence, decision support, Command and Control, Watchfloor, strategic decision making
