In strategic multi-agent sequential interactions, detecting dynamic coalition structures is crucial for understanding how self-interested agents coordinate to influence outcomes. However, natural-language-based interactions introduce unique challenges to coalition detection due to ambiguity over intents and difficulty in modeling players' subjective perspectives. We propose a new method that leverages recent advancements in large language models and game theory to predict dynamic multilateral coalition formation in Diplomacy, a strategic multi-agent game where agents negotiate coalitions using natural language. The method consists of two stages. The first stage extracts the set of agreements discussed by two agents in their private dialogue, by combining a parsing-based filtering function with a fine-tuned language model trained to predict player intents. In the second stage, we define a new metric using the concept of subjective rationalizability from hypergame theory to evaluate the expected value of an agreement for each player. We then compute this metric for each agreement identified in the first stage by assessing the strategic value of the agreement for both players and taking into account the subjective belief of one player that the second player would honor the agreement. We demonstrate that our method effectively detects potential coalition structures in online Diplomacy gameplay by assigning high values to agreements likely to be honored and low values to those likely to be violated. The proposed method provides foundational insights into coalition formation in multi-agent environments with language-based negotiation and offers key directions for future research on the analysis of complex natural language-based interactions between agents.

Coastal regions are particularly vulnerable to the impacts of rising sea levels and extreme weather events. Accurate real-time forecasting of hydrodynamic processes in these areas is essential for infrastructure planning and climate adaptation. In this study, we present the Multiple-Input Temporal Operator Network (MITONet), a novel autoregressive neural emulator that employs dimensionality reduction to efficiently approximate high-dimensional numerical solvers for complex, nonlinear problems that are governed by time-dependent, parameterized partial differential equations. Although MITONet is applicable to a wide range of problems, we showcase its capabilities by forecasting regional tide-driven dynamics described by the two-dimensional shallow-water equations, while incorporating initial conditions, boundary conditions, and a varying domain parameter. We demonstrate MITONet's performance in a real-world application, highlighting its ability to make accurate predictions by extrapolating both in time and parametric space.

Can simplifying AI rules in Europe create competition for US and China? Can simplifying AI rules in Europe create competition for US and China? Europe to cut red tape to make artificial intelligence advancements easier.Read more The Artificial Intelligence Action Summit in Paris has drawn nearly 100 world leaders and tech firms, and the consensus is that 2025 is not the year for new AI regulations. France says it is time to simplify the rules in Europe to allow AI advances – or risk being left behind. Which countries have banned DeepSeek and why? list 2 of 3 Elon Musk-led group makes 97.4bn bid for OpenAI list 3 of 3 In January, Chinese start-up DeepSeek disrupted Wall Street and Silicon Valley.

DeepSeek's new AI model is causing deep consternation from Silicon Valley to Washington. Few would have predicted that a little-known Chinese startup with a couple of hundred homegrown engineers would be able to release a frontier AI model rivaling the capabilities of America's best and biggest tech companies – reportedly at a fraction of the cost and computational power. Experts are hotly debating just how many and which type of chips DeepSeek used and whether the company stockpiled them or circumvented U.S. export controls. But the release and viral adoption of a Chinese AI competitor model has already rattled markets, highlighted the urgent competition for global brainpower, and caused some to ask whether all those billions that U.S. tech companies have spent buying chips and building data centers built a competitive moat or a Maginot line. This moment is game on, not game over.

Accurate and efficient analysis of multivariate spatio-temporal data is critical in climatology, geology, and sociology applications. Existing models usually assume simple inter-dependence among variables, space, and time, and are computationally expensive. We propose a unified low rank tensor learning framework for multivariate spatio-temporal analysis, which can conveniently incorporate different properties in spatio-temporal data, such as spatial clustering and shared structure among variables. We demonstrate how the general framework can be applied to cokriging and forecasting tasks, and develop an efficient greedy algorithm to solve the resulting optimization problem with convergence guarantee. We conduct experiments on both synthetic datasets and real application datasets to demonstrate that our method is not only significantly faster than existing methods but also achieves lower estimation error.

Three people, including a minor, were killed in a Ukrainian drone attack that hit a car in Logachyovka village in the Russian border region of Belgorod. Governor Vyacheslav Gladkov said a man, an 18-year-old woman and 14-year-old girl, all passengers, died in the attack. Ukraine's military said Kyiv's forces struck an airfield overnight in Russia's Krasnodar region, which sits on the Black Sea and Sea of Azov, resulting in explosions and a fire. According to the military, Moscow's forces use the airfield to store and launch drones to attack Ukraine and maintain aircraft that carry out missions in southern Ukraine. The military also said its army shot down 56 of 77 Russian drones launched at Ukraine overnight while 18 did not reach their targets.

This paper proposes a vision-in-the-loop simulation environment for deep monocular pose estimation of a UAV operating in an ocean environment. Recently, a deep neural network with a transformer architecture has been successfully trained to estimate the pose of a UAV relative to the flight deck of a research vessel, overcoming several limitations of GPS-based approaches. However, validating the deep pose estimation scheme in an actual ocean environment poses significant challenges due to the limited availability of research vessels and the associated operational costs. To address these issues, we present a photo-realistic 3D virtual environment leveraging recent advancements in Gaussian splatting, a novel technique that represents 3D scenes by modeling image pixels as Gaussian distributions in 3D space, creating a lightweight and high-quality visual model from multiple viewpoints. This approach enables the creation of a virtual environment integrating multiple real-world images collected in situ. The resulting simulation enables the indoor testing of flight maneuvers while verifying all aspects of flight software, hardware, and the deep monocular pose estimation scheme. This approach provides a cost-effective solution for testing and validating the autonomous flight of shipboard UAVs, specifically focusing on vision-based control and estimation algorithms.

The advancement in autonomous vehicles has empowered navigation and exploration in unknown environments. Geomagnetic navigation for autonomous vehicles has drawn increasing attention with its independence from GPS or inertial navigation devices. While geomagnetic navigation approaches have been extensively investigated, the generalizability of learned geomagnetic navigation strategies remains unexplored. The performance of a learned strategy can degrade outside of its source domain where the strategy is learned, due to a lack of knowledge about the geomagnetic characteristics in newly entered areas. This paper explores the generalization of learned geomagnetic navigation strategies via deep reinforcement learning (DRL). Particularly, we employ DRL agents to learn multiple teacher models from distributed domains that represent dispersed navigation strategies, and amalgamate the teacher models for generalizability across navigation areas. We design a reward shaping mechanism in training teacher models where we integrate both potential-based and intrinsic-motivated rewards. The designed reward shaping can enhance the exploration efficiency of the DRL agent and improve the representation of the teacher models. Upon the gained teacher models, we employ multi-teacher policy distillation to merge the policies learned by individual teachers, leading to a navigation strategy with generalizability across navigation domains. We conduct numerical simulations, and the results demonstrate an effective transfer of the learned DRL model from a source domain to new navigation areas. Compared to existing evolutionary-based geomagnetic navigation methods, our approach provides superior performance in terms of navigation length, duration, heading deviation, and success rate in cross-domain navigation. Geomagnetic navigation leverages the ubiquitous earth magnetic field signals for the navigation [1], [2], without independence on dedicated devices along the navigation route [3]-[5]. Geomagnetic navigation thus can secure the navigation mission, e.g., in remote areas or underwater environments where there GPS or pre-deployed navigation devices is unavailable [6].

Bad actors in the maritime industry engage in illegal behaviors after disabling their vessel's automatic identification system (AIS) - which makes finding such vessels difficult for analysts. Machine learning approaches only succeed in identifying the locations of these ``dark vessels'' in the immediate future. This work leverages ideas from the literature on abductive inference applied to locating adversarial agents to solve the problem. Specifically, we combine concepts from abduction, logic programming, and rule learning to create an efficient method that approaches full recall of dark vessels while requiring less search area than machine learning methods. We provide a logic-based paradigm for reasoning about maritime vessels, an abductive inference query method, an automatically extracted rule-based behavior model methodology, and a thorough suite of experiments.

President Donald Trump's controversially announced plans for the US to'take over and own' Gaza last night. While the proclamation drew criticism for'bringing more suffering to the region,' users on social media have used AI to transform the city into a gentrified metropolis with a large building featuring a'Trump Tower' sign glowing in lights at the city center. The rubble-filled streets were transformed into paved roadways lined with towering skyscrapers and areas where buildings had crumbled featured a green golf course surrounded by resorts. The AI-generated images were met with amusement, but others angered at the insensitivity of the creations and warned how'it would be the biggest blackpill ever if a great Biblical city was paved over.' Trump, who spent his career as a property developer, has long talked up Gaza's coastal location and pleasant climate as a perfect holiday vacation. In his vision, US reconstruction would create thousands of jobs and spare Palestinians the pain and expense of rebuilding once again.