I loved Board's unique blend of tabletop gaming, but my family gave it mixed reviews. Encourages everyone to play together at the table. Diverse launch titles show potential. Must be plugged in and includes a fairly short cable. This innovative mashup of digital and physical gaming worlds is a tabletop tablet designed to get family and friends playing together in the same room.

Analyzing decision problems under uncertainty commonly relies on idealizing assumptions about the describability of the world, with the most prominent examples being the closed world and the small world assumption. Most assumptions are operationalized by introducing states of the world, conditional on which the decision situation can be analyzed without any remaining uncertainty. Conversely, most classical decision-theoretic approaches are not applicable if the states of the world are inaccessible. We propose a decision model that retains the appeal and simplicity of the original theory, but completely overcomes the need to specify the states of the world explicitly. The main idea of our approach is to address decision problems in a radically empirical way: instead of specifying states and consequences prior to the decision analysis, we only assume a protocol of observed act--consequence pairs as model primitives. We show how optimality in such empirical decision problems can be addressed by using protocol-based empirical choice functions and discuss three approaches for deriving inferential guarantees: (I) consistent statistical estimation of choice sets, (II) consistent statistical testing of choice functions with robustness guarantees, and (III) direct inference for empirical choice functions using credal sets. We illustrate our theory with a proof-of-concept application comparing different prompting strategies in generative AI models.

Time flows ever onwards with reassuring uniformity - at least, that's how it feels to mere mortals unplugged from the weirder parts of physics. But everyone knows that the exception to this rule is the period between Christmas and New Year, in which time behaves strangely, moving like molasses until it lurches forwards as you near your return to work. If you usually misspend the twilight days of the year sitting idly in a fog of libations, you might be wondering how to occupy yourself. Fear not: staff and contributors have crafted a bucket list of all-time cultural greats to fill the long hours of the holiday season. It is an eclectic mix of books, films, television, music, video games, board games and more, designed to highlight some overlooked classics that you simply must try. The only thing they all have in common is their celebration of science, technology, the environment or any other topic you might find in . We hope you enjoy our favourites - if you choose to give one a go, your time will pass in the blink of an eye. Released in 2019, it broke from a stale formula of largely linear plotlines and choreographed cutscenes in the middle of gameplay, instead opting for narrative experimentation. You begin as a spacefaring alien in a solar system moments from destruction, stuck in a 22-minute time loop that ends with a supernova. It is also a physics lover's paradise: the game wrestles with quantum entanglement, entropy and non-Euclidean spaces. Its simulation of light bending around black holes is among the most accurate ever rendered in media.

While Theorem 1 indeed shows that many games in EFG (e.g. Appendix L: "Random Games of Skill") will have

Implementing board games in code can be a time-consuming task. However, Large Language Models (LLMs) have been proven effective at generating code for domain-specific tasks with simple contextual information. We aim to investigate whether LLMs can implement digital versions of board games from rules described in natural language. This would be a step towards an LLM-assisted framework for quick board game code generation. We expect to determine the main challenges for LLMs to implement the board games, and how different approaches and models compare to one another. We task three state-of-the-art LLMs (Claude, DeepSeek and ChatGPT) with coding a selection of 12 popular and obscure games in free-form and within Boardwalk, our proposed General Game Playing API. We anonymize the games and components to avoid evoking pre-trained LLM knowledge. The implementations are tested for playability and rule compliance. We evaluate success rate and common errors across LLMs and game popularity. Our approach proves viable, with the best performing model, Claude 3.7 Sonnet, yielding 55.6\% of games without any errors. While compliance with the API increases error frequency, the severity of errors is more significantly dependent on the LLM. We outline future steps for creating a framework to integrate this process, making the elaboration of board games more accessible.

One not to remember video game Uno. One not to remember video game Uno. Are digital board games as fun as the real thing? When our family board game night got cancelled, I sampled digital spins on the classics instead. I'm not sure I should have bothered - with one exception The whole point of video games is to be faster, more visually arresting, and less reliant on other humans than old games played with dice and cards. But a recent family board game night was derailed by clashing schedules and family civil war, so I spent a Saturday night trying them out on the iPhone instead.

Adversarial board games, as a paradigmatic domain of strategic reasoning and intelligence, have long served as both a popular competitive activity and a benchmark for evaluating artificial intelligence (AI) systems. Building on this foundation, we propose an adversarial benchmarking framework to assess the comprehensive performance of Large Language Models (LLMs) through board games competition, compensating the limitation of data dependency of the mainstream Question-and-Answer (Q&A) based benchmark method. We introduce Qi Town, a specialized evaluation platform that supports 5 widely played games and involves 20 LLM-driven players. The platform employs both the Elo rating system and a novel Performance Loop Graph (PLG) to quantitatively evaluate the technical capabilities of LLMs, while also capturing Positive Sentiment Score (PSS) throughout gameplay to assess mental fitness. The evaluation is structured as a round-robin tournament, enabling systematic comparison across players. Experimental results indicate that, despite technical differences, most LLMs remain optimistic about winning and losing, demonstrating greater adaptability to high-stress adversarial environments than humans. On the other hand, the complex relationship between cyclic wins and losses in PLGs exposes the instability of LLMs' skill play during games, warranting further explanation and exploration.