Expressive range analysis is a visualization-based technique used to evaluate the performance of generative models, particularly in game level generation. It typically employs two quantifiable metrics to position generated artifacts on a 2D plot, offering insight into how content is distributed within a defined metric space. In this work, we use the expressive range of a generator as the conceptual space of possible creations. Inspired by the quality diversity paradigm, we explore this space to generate levels. To do so, we use a constraint-based generator that systematically traverses and generates levels in this space. To train the constraint-based generator we use different tile patterns to learn from the initial example levels. We analyze how different patterns influence the exploration of the expressive range. Specifically, we compare the exploration process based on time, the number of successful and failed sample generations, and the overall interestingness of the generated levels. Unlike typical quality diversity approaches that rely on random generation and hope to get good coverage of the expressive range, this approach systematically traverses the grid ensuring more coverage. This helps create unique and interesting game levels while also improving our understanding of the generator's strengths and limitations.

In the final days of our pre-Switch 2 world, Nintendo is trying to rethink how sharing games works. The biggest announcement from the company's latest Direct was its upcoming Virtual Game Cards feature, a new approach to sharing digital games that improves on the company's current system, but still carries limitations that keep it from feeling truly modern. Virtual Game Cards attempt to make digital games as easy to share as physical ones. That starts with the company visually representing games as "cards" and using the language of loading and ejecting them, and extends to how simple they are to share. Two Switch consoles logged into your Nintendo Account can share any digital game just by "ejecting" it from one and "loading" it on another.

Nintendo is overhauling how digital downloads work on Nintendo Switch and Switch 2 with a new feature it's calling "Virtual Game Cards." Virtual Game Cards, which the company said during Thursday's Nintendo Direct livestream will launch in late April, are designed to better mimic the flexibility of physical games. It works like this: After buying a digital version of a game, the virtual card is loaded onto the player's Switch. Players can load or "eject" these game cards; with two systems, a player could eject a game on one system and load it onto another to play from that handheld. Although players will need a local connection to do so, it allows them to swap multiple games between systems quickly.

Today's Nintendo Direct provided a surprising bit of software news. The company just announced something called Virtual Game Card, which is a way to make playing and sharing downloaded titles more convenient. As the name suggests, this system creates a digital simulacrum of a physical game card. This means that multi-Switch households will easily be able to start a game on one console and transfer to another without any real hassle. Nintendo says they want to make digital games as easy to use as physical game cards.

The topics of Artificial intelligence (AI) and especially Machine Learning (ML) are increasingly making their way into educational curricula. To facilitate the access for students, a variety of platforms, visual tools, and digital games are already being used to introduce ML concepts and strengthen the understanding of how AI works. We take a look at didactic principles that are employed for teaching computer science, define criteria, and, based on those, evaluate a selection of prominent existing platforms, tools, and games. Additionally, we criticize the approach of portraying ML mostly as a black-box and the resulting missing focus on creating an understanding of data, algorithms, and models that come with it. To tackle this issue, we present a concept that covers intermodal transfer, computational and explanatory thinking, ICE-T, as an extension of known didactic principles. With our multi-faceted concept, we believe that planners of learning units, creators of learning platforms and educators can improve on teaching ML.

News that GOG.com has delisted 29 games this month is a sobering reminder that at any moment the games you own could vanish from your PC game libraries at any time and there's not much you can do about it. Admittedly, GOG's games include titles that many gamers may not have heard about. But history has shown that this happens to well-known titles too and on platforms with millions of users like Steam and Origin. So how is it that something you've legitimately bought can be whipped away in a heartbeat? Don't we have consumer protection laws against that?

Introduced as battery-powered sticks that emit nicotine-infused vapor, vape pens have transformed into increasingly sophisticated entertainment devices. And that, researchers say, is a potentially huge problem. Disposable vapes gained small illuminated displays last year, typically to show how much battery life remained. In about six months, though, the displays grew to the size of a flip phone screen and came equipped with retro games similar to Pac-Man and Tetris -- all on a product that costs less than 20. The speed at which vapes advanced to include an interactive display, as well as the devices' potential appeal to young people, is raising concerns about nicotine addiction among teenagers, say UC Riverside researchers Man Wong and Prue Talbot.

Recently, procedural content generation has exhibited considerable advancements in the domain of 2D game level generation such as Super Mario Bros. and Sokoban through large language models (LLMs). To further validate the capabilities of LLMs, this paper explores how LLMs contribute to the generation of 3D buildings in a sandbox game, Minecraft. We propose a Text to Building in Minecraft (T2BM) model, which involves refining prompts, decoding interlayer representation and repairing. Facade, indoor scene and functional blocks like doors are supported in the generation. Experiments are conducted to evaluate the completeness and satisfaction of buildings generated via LLMs. It shows that LLMs hold significant potential for 3D building generation. Given appropriate prompts, LLMs can generate correct buildings in Minecraft with complete structures and incorporate specific building blocks such as windows and beds, meeting the specified requirements of human users.

The swift evolution of Large-scale Models (LMs), either language-focused or multi-modal, has garnered extensive attention in both academy and industry. But despite the surge in interest in this rapidly evolving area, there are scarce systematic reviews on their capabilities and potential in distinct impactful scenarios. This paper endeavours to help bridge this gap, offering a thorough examination of the current landscape of LM usage in regards to complex game playing scenarios and the challenges still open. Here, we seek to systematically review the existing architectures of LM-based Agents (LMAs) for games and summarize their commonalities, challenges, and any other insights. Furthermore, we present our perspective on promising future research avenues for the advancement of LMs in games. We hope to assist researchers in gaining a clear understanding of the field and to generate more interest in this highly impactful research direction. A corresponding resource, continuously updated, can be found in our GitHub repository.

Procedurally generating cities in Minecraft provides players more diverse scenarios and could help understand and improve the design of cities in other digital worlds and the real world. This paper presents a city generator that was submitted as an entry to the 2023 Edition of Minecraft Settlement Generation Competition for Minecraft. The generation procedure is composed of six main steps, namely vegetation clearing, terrain reshaping, building layout generation, route planning, streetlight placement, and wall construction. Three algorithms, including a heuristic-based algorithm, an evolving layout algorithm, and a random one are applied to generate the building layout, thus determining where to place different redstone style buildings, and tested by generating cities on random maps in limited time. Experimental results show that the heuristic-based algorithm is capable of finding an acceptable building layout faster for flat maps, while the evolving layout algorithm performs better in evolving layout for rugged maps. A user study is conducted to compare our generator with outstanding entries of the competition's 2022 edition using the competition's evaluation criteria and shows that our generator performs well in the adaptation and functionality criteria