China has confirmed it's joining forces with Russia to build a research station on the moon by 2035, which will rival NASA's Lunar Gateway. Confirmation of plans to build the International Lunar Research Station (ILRS) came on Friday from officials at China National Space Administration (CNSA), the country's national space agency. Russia and China aim to complete basic infrastructure construction for ILRS by 2035, Wu Yanhua, CNSA deputy director, told a briefing in Beijing. ILRS will rival NASA's Lunar Gateway, which is set to play a'vital' role in the US space agency's upcoming Artemis program. However, NASA's Lunar Gateway will only orbit the moon, while ILRS will have both an orbiter and a base on the lunar surface, as well as multiple exploration rovers.

An estimated 300,000 meteorites could be sitting undiscovered within the ice fields of Antarctica, according to the findings of a new study. Using artificial intelligence to predict potential landing sites of pieces of space rock over the past few millennia, helped experts from the Free University of Brussels in Belgium, to create a'treasure map' of places to find these valuable rocks. Meteorites that fall in Antarctica typically become embedded in the ice sheet, making them harder to spot, but it seems many are hidden in plain sight. Two-thirds of all meteorites found on Earth have been discovered on the frozen continent, a process made easier due to the contrast between dark rocks and snow, with many discovered by chance during costly reconnaissance missions. In this new study, the team discovered that areas of'blue ice', where frozen water is visible at the surface as ice rather than snow, could be rich in meteorites.

Liu Boming took in the dizzy view. Around him lay the inky vastness of space. Over the next seven hours Liu and his colleague Tang Hongbo carried out China's second spacewalk, helped along by a giant robotic arm. Mission accomplished, the two taikonauts – China's astronauts – clambered back into their home for the next three months: Beijing's new space station. The core module of the station, named Tiangong, meaning "heavenly palace", was launched in April.

Competitive board games have provided a rich and diverse testbed for artificial intelligence. This paper contends that collaborative board games pose a different challenge to artificial intelligence as it must balance short-term risk mitigation with long-term winning strategies. Collaborative board games task all players to coordinate their different powers or pool their resources to overcome an escalating challenge posed by the board and a stochastic ruleset. This paper focuses on the exemplary collaborative board game Pandemic and presents a rolling horizon evolutionary algorithm designed specifically for this game. The complex way in which the Pandemic game state changes in a stochastic but predictable way required a number of specially designed forward models, macro-action representations for decision-making, and repair functions for the genetic operations of the evolutionary algorithm. Variants of the algorithm which explore optimistic versus pessimistic game state evaluations, different mutation rates and event horizons are compared against a baseline hierarchical policy agent. Results show that an evolutionary approach via short-horizon rollouts can better account for the future dangers that the board may introduce, and guard against them. Results highlight the types of challenges that collaborative board games pose to artificial intelligence, especially for handling multi-player collaboration interactions.

Academic research in board game playing AI has of course moved While artificial intelligence has been applied to control players' beyond most pedestrian board games, applying a diverse set of decisions in board games for over half a century, little attention algorithms for playing card games with millions of card combinations is given to games with no player competition. Pandemic is an exemplar such as Magic: the Gathering (Wizards of the Coast, 1993) [3], collaborative board game where all players coordinate to games of tactical card placement such as Lords of War (Black Box, overcome challenges posed by events occurring during the game's 2012) [19] and Carcassonne (Hans im Glück, 2000) [9], card games progression. This paper proposes an artificial agent which controls of team-based competition such as Hanabi (Abacusspiele, 2010) [26] all players' actions and balances chances of winning versus risk or Codenames (Czech Games Edition, 2015) [22], and many more. of losing in this highly stochastic environment. The agent applies Traditional board games such as chess [15] and backgammon a Rolling Horizon Evolutionary Algorithm on an abstraction of [23], as well as recent card games such as Race for the Galaxy (Rio the game-state that lowers the branching factor and simulates the Grande, 2007) [6] or digitized board games such as Hearthstone game's stochasticity. Results show that the proposed algorithm (Blizzard, 2014) [11, 18], focus on players competing to deplete another can find winning strategies more consistently in different games player's resources (pawns, hit points) or to accumulate more of varying difficulty.

When the first human explorers head for Mars, they're likely to have a non-human judging their performance and tweaking their interpersonal relationships when necessary. NASA and outside researchers are already working on artificial intelligence agents to monitor how future long-duration space crews interact, sort of like the holographic doctor on "Star Trek: Voyager." But there'll also be a need for the human touch -- in the form of crew members who could serve the roles of social directors or easygoing jokesters. That's the upshot of research initiatives discussed over the weekend here at the annual meeting of the American Association for the Advancement of Science. Using AI to assess astronauts' mental state is the focus of a NASA program known as Human Capabilities Assessments for Autonomous Missions, or H-CAAM, said Tom Williams, a researcher at NASA's Johnson Space Center who concentrates on human factors and performance for the space agency's Human Research Program.

Now, a collaboration between the labs of Princeton professors Mala Murthy and Joshua Shaevitz has gone a step further, using the latest advances in artificial intelligence (AI) to automatically track animals' individual body parts in existing video. Their new tool, LEAP Estimates Animal Pose (LEAP), can be trained in a matter of minutes to automatically track an animal's individual body parts over millions of frames of video with high accuracy, without having to add any physical markers or labels. "The method can be used broadly, across animal model systems, and it will be useful to measuring the behavior of animals with genetic mutations or following drug treatments," said Murthy, an associate professor of molecular biology and the Princeton Neuroscience Institute (PNI). The paper detailing the new technology will be published in the January 2019 issue of the journal Nature Methods, but its open-access version, released in May, has already led to the software being adopted by a number of other labs. When the researchers combine LEAP with other quantitative tools developed in their labs, they can study what they call "the language of behavior" by observing patterns in animal body movements, said Shaevitz, a professor of physics and the Lewis-Sigler Institute for Integrative Genomics.

The first sweet pepper harvesting robot in the world has been demonstrated at a greenhouse in Belgium. It could enter mainstream commercial use within four or five years. A pepper harvesting robot was introduced this month at the Research Station for Vegetable Production at St. Katelijne Waver in Belgium. Developed by a consortium including Ben-Gurion University of the Negev (BGU), SWEEPER is designed to operate in a single stem row cropping system, with non-clustered fruits and little leaf occlusion. Preliminary test results show that the robot currently harvests ripe fruit in an average time of 24 seconds.

Yesterday, the Halley VI Research Station was forced to close its Antarctic research base amid rising fears it could fall into a huge ice chasm. Shocking new drone footage has now been released that shows just how massive the growing crack in the ice is. The worrying footage has forced the British research base to relocate 14 miles (22 km) across the Brunt Ice Shelf and close its doors for the winter. The footage shows a 25 mile-long (40km) crack that appears to be a few feet deep. In some areas, the crack has split into two, leaving behind small islands of ice.