Although General Game Playing (GGP) systems can facilitate useful research in Artificial Intelligence (AI) for game-playing, they are often computationally inefficient and somewhat specialised to a specific class of games. However, since the start of this year, two General Game Systems have emerged that provide efficient alternatives to the academic state of the art -- the Game Description Language (GDL). In order of publication, these are the Regular Boardgames language (RBG), and the Ludii system. This paper offers an experimental evaluation of Ludii. Here, we focus mainly on a comparison between the two new systems in terms of two key properties for any GGP system: simplicity/clarity (e.g. human-readability), and efficiency.

The Digital Ludeme Project (DLP) aims to reconstruct and analyse over 1000 traditional strategy games using modern techniques. One of the key aspects of this project is the development of Ludii, a general game system that will be able to model and play the complete range of games required by this project. Such an undertaking will create a wide range of possibilities for new AI challenges. In this paper we describe many of the features of Ludii that can be used. This includes designing and modifying games using the Ludii game description language, creating agents capable of playing these games, and several advantages the system has over prior general game software.

Classical causal and statistical inference methods typically assume the observed data consists of independent realizations. However, in many applications this assumption is inappropriate due to a network of dependences between units in the data. Methods for estimating causal effects have been developed in the setting where the structure of dependence between units is known exactly, but in practice there is often substantial uncertainty about the precise network structure. This is true, for example, in trial data drawn from vulnerable communities where social ties are difficult to query directly. In this paper we combine techniques from the structure learning and interference literatures in causal inference, proposing a general method for estimating causal effects under data dependence when the structure of this dependence is not known a priori. We demonstrate the utility of our method on synthetic datasets which exhibit network dependence.

The highly influential framework of conceptual spaces provides a geometric way of representing knowledge. Instances are represented by points in a similarity space and concepts are represented by convex regions in this space. After pointing out a problem with the convexity requirement, we propose a formalization of conceptual spaces based on fuzzy star-shaped sets. Our formalization uses a parametric definition of concepts and extends the original framework by adding means to represent correlations between different domains in a geometric way. Moreover, we define various operations for our formalization, both for creating new concepts from old ones and for measuring relations between concepts. We present an illustrative toy-example and sketch a research project on concept formation that is based on both our formalization and its implementation.

A Los Angeles police officer wears an Axon body camera in 2017. On Thursday, the company announced it is holding off on facial recognition software, citing its unreliability. A Los Angeles police officer wears an Axon body camera in 2017. On Thursday, the company announced it is holding off on facial recognition software, citing its unreliability. The largest manufacturer of police body cameras is rejecting the possibility of selling facial recognition technology – at least, for now.

Computers use algorithms to do everything from adding up a column of figures to resizing a window. Computers use algorithms to do everything from adding up a column of figures to resizing a window. Algorithms were around for a very long time before the public paid them any notice. The word itself is derived from the name of a 9th-century Persian mathematician, and the notion is simple enough: an algorithm is just any step-by-step procedure for accomplishing some task, from making the morning coffee to performing cardiac surgery. Computers use algorithms for pretty much everything they do -- adding up a column of figures, resizing a window, saving a file to disk.

You're part of an underground network of feminists in Chicago that provide illegal (at the time) abortion services to vulnerable, pregnant people with few options. Despite the risk of imprisonment, and the ways that your personal experiences may not always perfectly align with your activism, you persist. It's a live-action roleplaying game by Jon Cole and Kelley Vanda called The Abortionists, which requires three players, one facilitator, six hours and a willingness to dig deep into the painful history of reproductive rights in the United States. That history has terrifying relevance in 2019, as numerous states pass laws that put their residents in a reality where abortion is functionally illegal. Based on the real-life work of a 1970s activist group called Jane, it challenges its participants to think about the "internal landscapes" of its players, and how they deal with the larger political and personal landscape of their world.

This month, about 50 feet (15 meters) under a Swiss glacier, you can experience what it might be like to live on the moon. Space agencies like NASA are looking to the moon as a waystation to venture beyond, to Mars and other cosmic destinations. One of the most viable spots for a lunar base could be inside a crater on the south pole of the moon, where scientists have confirmed the existence of water ice, a crucial resource for astronauts. To offer an idea at what such a habitat might look like, European researchers and students are conducting a mock moon habitat trial under a glacier near the famous Matterhorn in Switzerland's Alps. Called IGLUNA, the demonstration is organized by the Swiss Space Center and the European Space Agency.

WASHINGTON - For its next mission in our solar system, NASA plans to fly a drone copter to Saturn's largest moon, Titan, in search of the building blocks of life, the space agency said Thursday. The Dragonfly mission, which will launch in 2026 and land in 2034, will send a rotorcraft to fly to dozens of locations across the icy moon, which has a substantial atmosphere and is viewed by scientists as an equivalent of very early Earth. It is the only celestial body besides our planet known to have liquid rivers, lakes and seas on its surface, though these contain hydrocarbons like methane and ethane, not water. "Visiting this mysterious ocean world could revolutionize what we know about life in the universe, " said NASA administrator Jim Bridenstine. "This cutting-edge mission would have been unthinkable even just a few years ago, but we're now ready for Dragonfly's amazing flight."

In an unusual combination of disciplines, a basketball-shooting robot created by Japan's leading automaker helped students at a Tokyo elementary school on Friday to learn math. The physically active math lesson was joined by professional players from the B. League's Alvark Tokyo basketball team as well as Cue3, a humanoid robot made by one of the team's major sponsors, Toyota Motor Corp. The special class was part of Tokyo 2020 Math Drill, a learning program that incorporates 55 official sports from the Tokyo Olympics and Paralympics into math classes to provide fun learning opportunities. Sixth-graders at Fuchu Elementary School No. 10 in the city of Fuchu were divided into groups of 13 to 17 students. Each student shot the ball once and calculated the success rates for each group, making it an exercise in using fractions. The group that scored highest got to compete against players Daiki Tanaka and Joji Takeuchi.