Keepers realized the Bengal tiger was pregnant shortly before she gave birth. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The two males and one female came into the world on June 11. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

The evergreen trees give kangaroos, bison, lions, and more extra shelter and fun. Capybaras use donated Christmas trees as wind breaks to protect their habitats. Breakthroughs, discoveries, and DIY tips sent every weekday. The presents are unwrapped, the cookies are crumbs, and that real Christmas tree will become a fire hazard soon enough. Most of us haul it out to the curb for our local sanitation departments to take care of, but some lucky trees make it into the paws of animals living in zoos.

We propose a new General Game Playing (GGP) system called Regular Games (RG). The main goal of RG is to be both computationally efficient and convenient for game design. The system consists of several languages. The core component is a low-level language that defines the rules by a finite automaton. It is minimal with only a few mechanisms, which makes it easy for automatic processing (by agents, analysis, optimization, etc.). The language is universal for the class of all finite turn-based games with imperfect information. Higher-level languages are introduced for game design (by humans or Procedural Content Generation), which are eventually translated to a low-level language. RG generates faster forward models than the current state of the art, beating other GGP systems (Regular Boardgames, Ludii) in terms of efficiency. Additionally, RG's ecosystem includes an editor with LSP, automaton visualization, benchmarking tools, and a debugger of game description transformations.

Militia leader Stewart Rhodes, who was convicted for his role in the January 6 attack, is asking potential new members and supporters to send money. Stewart Rhodes announced last week that he is relaunching the Oath Keepers, his anti-government militia which virtually disappeared after dozens of its members--including Rhodes--were arrested for their roles in the January 6 attack on the Capitol . Rhodes, speaking to the Gateway Pundit this week, says that he sees the relaunched group as playing a role in combating what he labeled an "insurrection by the left" on the streets of US cities. "Right now, under federal statutes, president Trump can call us up as the militia if he sees it necessary, especially for three purposes: to repel invasions, to suppress insurrections, and to execute the laws of the union," Rhodes said. But in the days since Rhodes announced their return, experts, former members, and online chatter suggest there is little to no interest in restarting what was, at one point, one of the largest militias in America with a leaked database listing 38,000 supposed members in 2021. This hasn't stopped Rhodes from asking potential new members and supporters to send money in support of the cause.

Rozi was born in late February at Western Plains Zoo in Dubbo, Australia, by emergency caesarean after her mother, Siri, went into labour early. Lions' record-breaking swim across channel captured by drone camera Siri never produced milk, and because Rozi was critically unwell for the first weeks of her life, the mother and daughter had to be separated. Rozi's two siblings were stillborn, so as the sole surviving cub, she faced the prospect of at least 18 months in isolation, leaving her ill-equipped to join the zoo's breeding programme. Zookeepers decided that if Rozi was going to develop and socialise normally, giving her the best chance of successfully breeding one day, she would need a step-sibling. Unmissable news about our planet, delivered straight to your inbox each month.

Sea Life Melbourne Aquarium celebrates their star penguin, Pesto, who weighs a whopping 49 pounds. PENGUIN-INSPIRED ROBOT EXPLORES SEA USING AI Pesto weighs more than both his proud parents combined at a staggering 49 pounds. His parents, Hudson and Tango, each weigh about 24 pounds. According to a statement from the Sea Life Melbourne Aquarium, Pesto is the heaviest chick the facility has ever had. His gender was announced to the world earlier this month when his keeper, Michaela Smale, "shovel[ed] away a mountain of fresh snow to unleash an avalanche of blue."

Robots are increasingly common in both industry and daily life, such as in nursing homes where they can assist staff. A key challenge is developing intuitive interfaces for easy communication. The use of Large Language Models (LLMs) like GPT-4 has enhanced robot capabilities, allowing for real-time interaction and decision-making. This integration improves robots' adaptability and functionality. This project focuses on merging LLMs with databases to improve decision-making and enable knowledge acquisition for the request interpretation problems.

This work stages Foosball as a versatile platform for advancing scientific research, particularly in the realm of robot learning. We present an automated Foosball table along with its corresponding simulated counterpart, showcasing a diverse range of challenges through example tasks within the Foosball environment. Initial findings are shared using a simple baseline approach. Foosball constitutes a versatile learning environment with the potential to yield cutting-edge research in various fields of artificial intelligence and machine learning, notably robust learning, while also extending its applicability to industrial robotics and automation setups. To transform our physical Foosball table into a research-friendly system, we augmented it with a 2 degrees of freedom kinematic chain to control the goalkeeper rod as an initial setup with the intention to be extended to the full game as soon as possible. Our experiments reveal that a realistic simulation is essential for mastering complex robotic tasks, yet translating these accomplishments to the real system remains challenging, often accompanied by a performance decline. This emphasizes the critical importance of research in this direction. In this concern, we spotlight the automated Foosball table as an invaluable tool, possessing numerous desirable attributes, to serve as a demanding learning environment for advancing robotics and automation research.

Structured prediction problems are one of the fundamental tools in machine learning. In order to facilitate algorithm development for their numerical solution, we collect in one place a large number of datasets in easy to read formats for a diverse set of problem classes. We provide archival links to datasets, description of the considered problems and problem formats, and a short summary of problem characteristics including size, number of instances etc. For reference we also give a non-exhaustive selection of algorithms proposed in the literature for their solution. We hope that this central repository will make benchmarking and comparison to established works easier. We welcome submission of interesting new datasets and algorithms for inclusion in our archive.

Even though Google Research Football (GRF) was initially benchmarked and studied as a single-agent environment in its original paper, recent years have witnessed an increasing focus on its multi-agent nature by researchers utilizing it as a testbed for Multi-Agent Reinforcement Learning (MARL). However, the absence of standardized environment settings and unified evaluation metrics for multi-agent scenarios hampers the consistent understanding of various studies. Furthermore, the challenging 5-vs-5 and 11-vs-11 full-game scenarios have received limited thorough examination due to their substantial training complexities. To address these gaps, this paper extends the original environment by not only standardizing the environment settings and benchmarking cooperative learning algorithms across different scenarios, including the most challenging full-game scenarios, but also by discussing approaches to enhance football AI from diverse perspectives and introducing related research tools. Specifically, we provide a distributed and asynchronous population-based self-play framework with diverse pre-trained policies for faster training, two football-specific analytical tools for deeper investigation, and an online leaderboard for broader evaluation. The overall expectation of this work is to advance the study of Multi-Agent Reinforcement Learning on Google Research Football environment, with the ultimate goal of benefiting real-world sports beyond virtual games.