Autonomous drone technology holds significant promise for enhancing search and rescue operations during evacuations by guiding humans toward safety and supporting broader emergency response efforts. However, their application in dynamic, real-time evacuation support remains limited. Existing models often overlook the psychological and emotional complexity of human behavior under extreme stress. In real-world fire scenarios, evacuees frequently deviate from designated safe routes due to panic and uncertainty. To address these challenges, this paper presents a multi-agent coordination framework in which autonomous Unmanned Aerial Vehicles (UAVs) assist human evacuees in real-time by locating, intercepting, and guiding them to safety under uncertain conditions. We model the problem as a Partially Observable Markov Decision Process (POMDP), where two heterogeneous UAV agents, a high-level rescuer (HLR) and a low-level rescuer (LLR), coordinate through shared observations and complementary capabilities. Human behavior is captured using an agent-based model grounded in empirical psychology, where panic dynamically affects decision-making and movement in response to environmental stimuli. The environment features stochastic fire spread, unknown evacuee locations, and limited visibility, requiring UAVs to plan over long horizons to search for humans and adapt in real-time. Our framework employs the Proximal Policy Optimization (PPO) algorithm with recurrent policies to enable robust decision-making in partially observable settings. Simulation results demonstrate that the UAV team can rapidly locate and intercept evacuees, significantly reducing the time required for them to reach safety compared to scenarios without UAV assistance.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A missing hiker's dead body was finally found in July in Italy's rugged Piedmont region after 10 months. The recovery team credited the breakthrough to an AI-powered drone that spotted a critical clue within hours. The same process would have taken weeks or even months if done by the human eye.

This paper explores the emergence of language in multi-agent reinforcement learning (MARL) using transformers. Existing methods such as RIAL, DIAL, and CommNet enable agent communication but lack interpretability. We propose Differentiable Inter-Agent Transformers (DIAT), which leverage self-attention to learn symbolic, human-understandable communication protocols. Through experiments, DIAT demonstrates the ability to encode observations into interpretable vocabularies and meaningful embeddings, effectively solving cooperative tasks.

In urban search and rescue (USAR) operations, communication between handlers and specially trained canines is crucial but often complicated by challenging environments and the specific behaviors canines are trained to exhibit when detecting a person. Since a USAR canine often works out of sight of the handler, the handler lacks awareness of the canine's location and situation, known as the 'sensemaking gap.' In this paper, we propose KHAIT, a novel approach to close the sensemaking gap and enhance USAR effectiveness by integrating object detection-based Artificial Intelligence (AI) and Augmented Reality (AR). Equipped with AI-powered cameras, edge computing, and AR headsets, KHAIT enables precise and rapid object detection from a canine's perspective, improving survivor localization. We evaluate this approach in a real-world USAR environment, demonstrating an average survival allocation time decrease of 22%, enhancing the speed and accuracy of operations.

A Russian drone attack on an apartment block in the southern Ukrainian port city of Odesa has killed at least seven people, including a three-year-old and a woman with an infant child, regional authorities said. "Rescuers in Odesa have just uncovered the bodies of a mother with a three-month-old baby," Interior Minister Ihor Klymenko said in a post on the Telegram app on Saturday. At the scene, smoke poured from rubble strewn across the ground where the drone had ripped a chunk several storeys high out of the building. Clothes and furniture were scattered in the ruined mass of concrete and steel hanging off the side of the apartment block. Ukraine's State Emergencies Service posted photos, including of a dead toddler being placed in a body bag by rescuers.

When a devastating earthquake struck Turkey in the early hours of February 6, 2023, the five-storey building in Hatay where 13-year-old Mehmet Koc lived, collapsed, burying him in rubble and killing his older brother Emre, 14, and his mother Didem. But it took 76 hours before rescuers could pull him from the mound of concrete and twisted metal that remained of his home. Later in hospital, doctors determined that his legs were so badly crushed and injured, that both needed to be amputated just below the hip. Hearing of the earthquake in London where he lived and worked, Mehmet's father, Hasan, caught the next available flight to Turkey and travelled to Hatay, in the southeast, desperate for news of his family. The 58-year-old encountered a scene of utter destruction.

At least 10 people, including a child, have been killed and 45 injured following a Ukrainian attack on the centre of the Russian provincial capital of Belgorod, the Russian Emergencies Ministry has said. Governor Vyacheslav Gladkov said on Saturday that the attack on Belgorod, about 30km (19 miles) from the border with Ukraine, had hit a residential area. In a Telegram post, he urged all residents to move to air raid shelters as sirens sounded. Belgorod borders Ukraine's Luhansk, Sumy and Kharkiv regions, some of which were hit by Russian air raids on Ukraine on Friday, in what was one of the deadliest attacks since the war began in February 2022. The death toll has risen to 39 from those attacks.

Current methods of deploying robots that operate in dynamic, uncertain environments, such as Uncrewed Aerial Systems in search \& rescue missions, require nearly continuous human supervision for vehicle guidance and operation. These methods do not consider high-level mission context resulting in cumbersome manual operation or inefficient exhaustive search patterns. We present a human-centered autonomous framework that infers geospatial mission context through dynamic feature sets, which then guides a probabilistic target search planner. Operators provide a set of diverse inputs, including priority definition, spatial semantic information about ad-hoc geographical areas, and reference waypoints, which are probabilistically fused with geographical database information and condensed into a geospatial distribution representing an operator's preferences over an area. An online, POMDP-based planner, optimized for target searching, is augmented with this reward map to generate an operator-constrained policy. Our results, simulated based on input from five professional rescuers, display effective task mental model alignment, 18\% more victim finds, and 15 times more efficient guidance plans then current operational methods.

The past few years have seen an uptick of interest in studying cooperative AI, that is, AI systems that are designed to be effective at cooperating. Indeed, a number of influential researchers recently argued that "[w]e need to build a science of cooperative AI... progress towards socially valuable AI will be stunted unless we put the problem of cooperation at the centre of our research" [6]. One type of cooperative behavior is joint behavior, that is, collaboration scenarios where the success of the joint action is dependent on all agents doing their parts; one agent deviating can cause the efforts of others to be ineffective. The notion of joint behavior has been studied (in much detail) under various names such as "acting together", "teamwork", "collaborative plans", and "shared plans", and highly influential models of it were developed (see, e.g., [2, 4, 10, 11, 15, 24]). Efforts were also made to engineer some of these theories into real-world joint planning systems [23, 20].

A humpback whale caught in a fishing rope off the western coast of Canada stunned rescuers when it pulled off a magnificent maneuver to free itself. A humpback whale entangled in fishing gear off the coast of western Canada was caught on video pulling off a spectacular maneuver to free itself as rescuers worked to help the sea creature. The ocean mammal was caught in the ropes of a buoy used to catch prawn for two days when rescuers caught up with the whale near Texada Island on Oct. 14, Fisheries and Oceans Canada said. The department's Marine Mammal Rescue team was following the distressed whale when its aerial drone spotted two more humpback whales swimming alongside the creature. Before rescuers attempted to cut the rope that was caught in the animal's mouth, they added some drag to slow the whale down, Paul Cottrell, with Fisheries and Oceans Canada, told the BBC.