Multi-agent reinforcement learning (MARL) is a widely used Artificial Intelligence (AI) technique. However, current studies and applications need to address its scalability, non-stationarity, and trustworthiness. This paper aims to review methods and applications and point out research trends and visionary prospects for the next decade. First, this paper summarizes the basic methods and application scenarios of MARL. Second, this paper outlines the corresponding research methods and their limitations on safety, robustness, generalization, and ethical constraints that need to be addressed in the practical applications of MARL. In particular, we believe that trustworthy MARL will become a hot research topic in the next decade. In addition, we suggest that considering human interaction is essential for the practical application of MARL in various societies. Therefore, this paper also analyzes the challenges while MARL is applied to human-machine interaction.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. An airstrike targeted a militant group in northern Iraq's Yazidi heartland of Sinjar on Tuesday, according to local officials, who attributed the strike to Turkey. Officials gave conflicting reports regarding the number of casualties. The semi-autonomous Kurdish region's counter-terrorism service said in a statement that three fighters were killed in the attack, and one wounded.

Altman advocated for a number of regulations, including a new government agency charged with creating government standards for the field, to address mounting concerns that generative AI could distort reality and create unprecedented safety risks. The CEO tallied a litany of "risky" behaviors presented by technology like ChatGPT, including spreading "one-on-one interactive disinformation" and emotional manipulation. At one point he acknowledged AI could be used to target drone strikes.

Accurate prediction of aerodynamic forces in real-time is crucial for autonomous navigation of unmanned aerial vehicles (UAVs). This paper presents a data-driven aerodynamic force prediction model based on a small number of pressure sensors located on the surface of UAV. The model is built on a linear term that can make a reasonably accurate prediction and a nonlinear correction for accuracy improvement. The linear term is based on a reduced basis reconstruction of the surface pressure distribution, where the basis is extracted from numerical simulation data and the basis coefficients are determined by solving linear pressure reconstruction equations at a set of sensor locations. Sensor placement is optimized using the discrete empirical interpolation method (DEIM). Aerodynamic forces are computed by integrating the reconstructed surface pressure distribution. The nonlinear term is an artificial neural network (NN) that is trained to bridge the gap between the ground truth and the DEIM prediction, especially in the scenario where the DEIM model is constructed from simulation data with limited fidelity. A large network is not necessary for accurate correction as the linear model already captures the main dynamics of the surface pressure field, thus yielding an efficient DEIM+NN aerodynamic force prediction model. The model is tested on numerical and experimental dynamic stall data of a 2D NACA0015 airfoil, and numerical simulation data of dynamic stall of a 3D drone. Numerical results demonstrate that the machine learning enhanced model can make fast and accurate predictions of aerodynamic forces using only a few pressure sensors, even for the NACA0015 case in which the simulations do not agree well with the wind tunnel experiments. Furthermore, the model is robust to noise.

The United Kingdom has promised Ukraine further arms for its fight against Russia, as President Volodymyr Zelenskyy made a surprise stop to meet with his British counterpart. Zelenskyy landed by helicopter on Monday at Chequers Court, the British leader's official country retreat, where he was greeted by Prime Minister Rishi Sunak with a handshake and a hug. Sunak's office said that Britain was set to confirm it was giving Ukraine hundreds more air defence missiles, as well as "long-range attack drones" with a range of more than 200km (124 miles). Zelenskyy, on his second trip to the UK since Russia invaded his country in February 2022, thanked his staunch ally and said the war was a matter of "security not only for Ukraine, it is important for all of Europe." Sunak told Zelenskyy that "your leadership, your country's bravery and fortitude are an inspiration to us all".

One killed, 10 injured and dozens of homes damage after tornado strikes Laguna Heights, Texas. Drone footage has emerged capturing the aftermath of a deadly tornado that ripped through a Texas Gulf Coast town near the U.S.-Mexico border. The EF-1 twister that struck Laguna Heights early Saturday, located on the mainland across from South Padre Island, left one dead and 10 injured, officials said. Video taken by the Brownsville Fire Department shows the damage that was inflicted upon as many as 60 homes, with some missing roofs and others reduced to piles of rubble. Roberto Flores, 42, died after being "basically crushed as a result of the damage to his mobile home," according to Eddie Treviño Jr., a judge in Cameron County.

Several distributed algorithms are presented for the exploration of unknown indoor regions by a swarm of flying, energy constrained agents. The agents, which are identical, autonomous, anonymous and oblivious, uniformly cover the region and thus explore it using predefined action rules based on locally sensed information and the energy level of the agents. While flying drones have many advantages in search and rescue scenarios, their main drawback is a high power consumption during flight combined with limited, on-board energy. Furthermore, in these scenarios agent size is severely limited and consequently so are the total weight and capabilities of the agents. The region is modeled as a connected sub-set of a regular grid composed of square cells that the agents enter, over time, via entry points. Some of the agents may settle in unoccupied cells as the exploration progresses. Settled agents conserve energy and become virtual pheromones for the exploration and coverage process, beacons that subsequently aid the remaining, and still exploring, mobile agents. The termination of the coverage process is based on a backward propagating information diffusion scheme. Various algorithmical alternatives are discussed and upper bounds derived and compared to experimental results. Finally, an optimal entry rate that minimizes the total energy consumption is derived for the case of a linear regions.

Ultra-wideband (UWB) positioning has emerged as a low-cost and dependable localization solution for multiple use cases, from mobile robots to asset tracking within the Industrial IoT. The technology is mature and the scientific literature contains multiple datasets and methods for localization based on fixed UWB nodes. At the same time, research in UWB-based relative localization and infrastructure-free localization is gaining traction, further domains. tools and datasets in this domain are scarce. Therefore, we introduce in this paper a novel dataset for benchmarking infrastructure-free relative localization targeting the domain of multi-robot systems. Compared to previous datasets, we analyze the performance of different relative localization approaches for a much wider variety of scenarios with varying numbers of fixed and mobile nodes. A motion capture system provides ground truth data, are multi-modal and include inertial or odometry measurements for benchmarking sensor fusion methods. Additionally, the dataset contains measurements of ranging accuracy based on the relative orientation of antennas and a comprehensive set of measurements for ranging between a single pair of nodes. Our experimental analysis shows that high accuracy can be localization, but the variability of the ranging error is significant across different settings and setups.

Kurt "The Cyberguy" Knutsson explains how scientists managed to turn dead birds into drones that can potentially spy on people. You may start seeing more drones soaring through the air, and don't worry, it's not going to be from any secret spies trying to look into your home. These drones are going to be sent out for deliveries from major companies like Amazon, Walmart, Google, UPS, FedEx, Uber and DHL. CLICK TO GET KURT'S FREE CYBERGUY NEWSLETTER WITH QUICK TIPS, TECH REVIEWS, SECURITY ALERTS AND EASY HOW-TO'S TO MAKE YOU SMARTER Alphabet, Google's parent company, has been experimenting with drone delivery as part of its Project Wing initiative, which aims to create a fleet of unmanned aircraft that can deliver items from food to medical supplies. Walmart has also been testing drones to deliver groceries and household essentials in select markets since 2015.

Given the importance of forests and their role in maintaining the ecological balance, which directly affects the planet, the climate, and the life on this planet, this research presents the problem of forest fire monitoring using drones. The forest monitoring process is performed continuously to track any changes in the monitored region within the forest. During fires, drones' capture data is used to increase the follow-up speed and enhance the control process of these fires to prevent their spread. The time factor in such problems determines the success rate of the fire extinguishing process, as appropriate data at the right time may be the decisive factor in controlling fires, preventing their spread, extinguishing them, and limiting their losses. Therefore, this research presented the problem of monitoring task scheduling for drones in the forest monitoring system. This problem is solved by developing several algorithms with the aim of minimizing the total completion time required to carry out all the drones' assigned tasks. System performance is measured by using 990 instances of three different classes. The performed experimental results indicated the effectiveness of the proposed algorithms and their ability to act efficiently to achieve the desired goal. The algorithm $RID$ achieved the best performance with a percentage rate of up to 90.3% with a time of 0.088 seconds.