With the recent advances in machine learning, creating agents that behave realistically in simulated air combat has become a growing field of interest. This survey explores the application of machine learning techniques for modeling air combat behavior, motivated by the potential to enhance simulation-based pilot training. Current simulated entities tend to lack realistic behavior, and traditional behavior modeling is labor-intensive and prone to loss of essential domain knowledge between development steps. Advancements in reinforcement learning and imitation learning algorithms have demonstrated that agents may learn complex behavior from data, which could be faster and more scalable than manual methods. Yet, making adaptive agents capable of performing tactical maneuvers and operating weapons and sensors still poses a significant challenge. The survey examines applications, behavior model types, prevalent machine learning methods, and the technical and human challenges in developing adaptive and realistically behaving agents. Another challenge is the transfer of agents from learning environments to military simulation systems and the consequent demand for standardization. Four primary recommendations are presented regarding increased emphasis on beyond-visual-range scenarios, multi-agent machine learning and cooperation, utilization of hierarchical behavior models, and initiatives for standardization and research collaboration. These recommendations aim to address current issues and guide the development of more comprehensive, adaptable, and realistic machine learning-based behavior models for air combat applications.

Artificial intelligence (AI) has enormous potential to improve Air Force pilot training by providing actionable feedback to pilot trainees on the quality of their maneuvers and enabling instructor-less flying familiarization for early-stage trainees in low-cost simulators. Historically, AI challenges consisting of data, problem descriptions, and example code have been critical to fueling AI breakthroughs. The Department of the Air Force-Massachusetts Institute of Technology AI Accelerator (DAF-MIT AI Accelerator) developed such an AI challenge using real-world Air Force flight simulator data. The Maneuver ID challenge assembled thousands of virtual reality simulator flight recordings collected by actual Air Force student pilots at Pilot Training Next (PTN). This dataset has been publicly released at Maneuver-ID.mit.edu and represents the first of its kind public release of USAF flight training data. Using this dataset, we have applied a variety of AI methods to separate "good" vs "bad" simulator data and categorize and characterize maneuvers. These data, algorithms, and software are being released as baselines of model performance for others to build upon to enable the AI ecosystem for flight simulator training.

Vertical Aerospace, a global aerospace and technology company that is pioneering zero-emissions aviation and CAE, a market leader in flight simulation and training, announced that CAE will be the pilot training partner for Vertical's launch eVTOL aircraft, the VX4. CAE will design and develop a world-class training program and be the exclusive training device provider, tailoring the high-fidelity, next-generation flight simulation training device for the VX4 aircraft. The innovative pilot training program will leverage advanced technologies including Mixed Reality and Artificial Intelligence to enhance the learning experience and will help shift the training paradigm toward cost-effectiveness and scalability, while ensuring safety is paramount for Vertical and its operators. Advanced Air Mobility (AAM) is expected to drive unprecedented demand for qualified, professionally trained pilots for inner-city and regional electric flights. Additionally, because CAE currently provides training products and services to many of Vertical's industry-leading customer base, a smoother integration of new AAM pilot training programmes into their training portfolios is anticipated.

AI algorithms that identify maneuvers from trajectory data could play an important role in improving flight safety and pilot training. AI challenges allow diverse teams to work together to solve hard problems and are an effective tool for developing AI solutions. AI challenges are also a key driver of AI computational requirements. The Maneuver Identification Challenge hosted at maneuver-id.mit.edu provides thousands of trajectories collected from pilots practicing in flight simulators, descriptions of maneuvers, and examples of these maneuvers performed by experienced pilots. Each trajectory consists of positions, velocities, and aircraft orientations normalized to a common coordinate system. Construction of the data set required significant data architecture to transform flight simulator logs into AI ready data, which included using a supercomputer for deduplication and data conditioning. There are three proposed challenges. The first challenge is separating physically plausible (good) trajectories from unfeasible (bad) trajectories. Human labeled good and bad trajectories are provided to aid in this task. Subsequent challenges are to label trajectories with their intended maneuvers and to assess the quality of those maneuvers.

In the near future, an Air Force pilot's wingman could be flown by artificial intelligence. The two might fly side-by-side in a highly contested war zone -- and the AI aircraft not only takes the lead, it begins making choices. Does it fire missiles or drop bombs ahead of its fighter counterpart? Probably, because the human response has a lag time, unlike a machine that can detect and react immediately, if necessary. It sounds very much like the Air Force's proposed Loyal Wingman program.

The head of Air Force training said Tuesday that the service wants artificial intelligence to become the go-to coach that helps airmen learn faster and better than ever before. Lt. Gen. Steven Kwast, commander of Air Education and Training Command, said he hopes that the results of testing, scheduled to be completed next year, will show that futuristic tools such as AI, virtual reality and super-computing can improve the speed and effectiveness of the human brain. "The data is very promising that we can accentuate the adult human brain to learn faster, better and, I'll say, more sticky, meaning when you learn something longer and better," Kwast told a group of defense reporters at the Air Force Association's Air, Space & Cyber Conference. He used pilot training as an example of how artificial intelligence can be used as a coach in a flight simulator. "Let's take a loop: A pilot has to do a loop, and the artificial intelligence is watching you do that loop. And, as you pull back on the stick, it can tell what you are doing and says, 'Hey, you are pulling back too much. Keep your eye on the horizon,' " Kwast said.

This article is part of Inc42's Startup Watchlist annual series where we list the top startups to watch for 2018 from industries like AI, IoT, Blockchain etc. Explore all the stories from'Startup Watchlist' series here. Once used as a tool for an application, Internet of Things (IoT) has become one of the widest ecosystems today. Currently at the centre stage of industries like energy management, healthcare, logistics, fintech, manufacturing and agritech, IoT, in convergence with AI, has the potential to disrupt all these verticals. Previously dictated by big players like IBM, Google, Intel, Cisco, Ericsson, Apple and Amazon, the IoT space has now become a startup ecosystem enabler across the world. While it was the Internet that drove the emergence of ecommerce startups in the early 2000s, IoT has been facilitating the growth of this decade's tech startups. What lightning does to mushrooms, IoT has done to startups!