The U.S. Navy and U.S. Air Force are working on a new generation of training technologies to prepare their fighter aircraft for new Russian and Chinese air threats posed by the Su-57 fighter and J-20 fifth-generation stealth aircraft, respectively. Over the next two years, the U.S. Air Force plans to use a cutting-edge computer technology called the P5 Combat Training System (P5CTS), made by a firm called Cubic Mission and Performance Solutions. Information from Cubic describes the P5 as an encryption solution intended to improve U.S. Air Force and U.S. Navy pilot training for advanced, high-threat combat scenarios using advanced computer simulations, wireless networks, and artificial intelligence (AI)-enabled data organization. Interestingly, the P5 pod can be seen in the now-famous Top Gun Maverick movie on a F/A-18 fighter. "Over the course of the last 13 years, we've learned some critical lessons about integrating fast movers with virtual environments to provide a realistic presentation to the aircrew in their cockpits. Having a wireless network that allows you to sustain that environment without interruptions. In other words, a low, flat latency is a very important feature," said Cubic's training expert, Paul Averna.

Therefore, our work alliances through multiple means: diplomatic, economic, seeks to advance capabilities in explainable AI/ML to allow conventional and unconventional warfare, including information a human operative to'calibrate their trust' in an AI/ML asset warfare. A critical requirement for allies is potentially provided by a different coalition partner (Tomsett rapid and continuous integration of capabilities to collect, et al. 2020). The purpose of human-machine teaming is process, disseminate and exploit actionable information and to aim for each party to exploit the strengths of, and compensate intelligence. To achieve this, the MDO layered ISR concept for the weaknesses of, the other (Cummings 2014).

Generative Policy-based Models aim to enable a coalition of systems, be they devices or services to adapt according to contextual changes such as environmental factors, user preferences and different tasks whilst adhering to various constraints and regulations as directed by a managing party or the collective vision of the coalition. Recent developments have proposed new architectures to realize the potential of GPMs but as the complexity of systems and their associated requirements increases, there is an emerging requirement to have scenarios and associated datasets to realistically evaluate GPMs with respect to the properties of the operating environment, be it the future battlespace or an autonomous organization. In order to address this requirement, in this paper, we present a method of applying an agile knowledge representation framework to model requirements, both individualistic and collective that enables synthetic generation of ground truth data such that advanced GPMs can be evaluated robustly in complex environments. We also release conceptual models, annotated datasets, as well as means to extend the data generation approach so that similar datasets can be developed for varying complexities and different situations.

WASHINGTON – The imminent fall of the Islamic State's de facto capital leaves America a multitude of tasks to restore stability in the Middle East, starting with pockets of remaining IS resistance in Syria and Iraq. Then there are the more deeply rooted problems, not fixable by guns or bombs, that allowed extremism to rise and flourish: Syria's civil war and Iraq's intractable political, religious and ethnic disputes, which turned violent again this week. The challenge is more than the U.S. can handle alone. It likely will keep some troops in Iraq for years to come to train and advise the army, police and other members of security forces that imploded when IS fighters swept across the Syrian border and captured Mosul in June 2014. The militants also have footholds in Afghanistan and beyond.

Forming ad-hoc coalitions between military forces and humanitarian organizations is crucial in mission-critical scenarios. Very often coalition parties need to operate according to planning constraints and regulations, or policies. Therefore, they find themselves not only in need to consider their own goals, but also to support coalition partners to the extent allowed by such regulations. In time-stressed conditions, this is a challenging and cognition-intensive task. In this paper, we present intelligent agents that support human planners and ease their cognitive burden by detecting and giving advice about the violation of policies and constraints. Through a series of experiments conducted with human subjects, we compare and contrast the agents' performance on a number of metrics in three conditions: agent support, transparent policy enforcement, and neither support nor enforcement.