Large language models (LLMs) have recently demonstrated great success in generating and understanding natural language. While they have also shown potential beyond the domain of natural language, it remains an open question as to what extent and in which way these LLMs can plan. We investigate their planning capabilities by proposing \texttt{GameTraversalBenchmark (GTB)}, a benchmark consisting of diverse 2D grid-based game maps. An LLM succeeds if it can traverse through given objectives, with a minimum number of steps and a minimum number of generation errors. We evaluate a number of LLMs on \texttt{GTB} and found that GPT-4-Turbo achieved the highest score of 44.97\% on \texttt{GTB\_Score} (GTBS), a composite score that combines the three above criteria.

"Duck" and "ghost" character sprites generated by an AI A simple generative AI tool can create video game maps, character models and emojis from a single-sentence prompt within milliseconds. Julian Togelius and Timothy Merino at New York University and their colleagues designed the system as a way of understanding how simple an AI model can be while still proving useful. "We tried as a starting point to figure out the most naive, simple approach we could do for textbook map generation," says Merino. The model was trained on databases of 882 game maps, 100 game sprites and 10,000 emojis, all of which were labelled with descriptions of what the images showed. "All of our datasets were fairly small, and that was by design," says Merino.

The first thing to do when studying an algorithm is to understand the data. Input: Graph search algorithms, including A*, take a "graph" as input. A graph is a set of locations ("nodes") and the connections ("edges") between them. Here's the graph I gave to A*: A* doesn't see anything else. It only sees the graph.

Path planning is a critical part of modern computer games; rare is the game where nothing moves and path planning is unneeded. A* is the workhorse for most path planning applications. Block A* is a state-of-the-art algorithm that is always faster than A* in experiments using game maps. Unlike other methods that improve upon A*'s performance, Block A* is never worse than A* nor require any knowledge of the map. In our experiments, Block A* is ideal for games with randomly generated maps, large maps, or games with a highly dynamic multi-agent environment. Furthermore, in the domain of grid-based any-angle path planning, we show that Block A* is an order of magnitude faster than the previous best any-angle path planning algorithm, Theta*. We empirically show our results using maps from Dragon Age: Origins and Starcraft. Finally, we introduce populated game maps'' as a new test bed that is a better approximation of real game conditions than the standard test beds of this field. The main contributions of this paper is a more rigorous set of experiments for Block A*, and introducing a new test bed (populated game maps) that is a more accurate representation of actual game conditions than the standard test beds.

Video game worlds are getting increasingly large and complex. This poses challenges to the game AI for both pathfinding and strategic decisions, not least in real-time strategy games. One way to alleviate the problem is to manually pre-label the game maps with information about regions and critical choke points, which the game AI can then take advantage of. We present a method for automatically decomposing game maps into non-uniform sized regions. The method uses a flooding algorithm at its core and has the benefit, in addition to its effectiveness, to be relatively intuitive both conceptually and in implementing. Empirical evaluation on game maps shows that the automatic decomposition results in intuitive regions of a comparable standard to human-made labeling. Furthermore, we show that our automatic decomposition, when used by a pathfinding algorithm capable of taking advantage of pre-labeled regions, significantly improves search effectiveness.

In August, Tencent announced it had developed an AI system capable of defeating teams of pros in a five-on-five match in Honor of Kings (or Arena of Valor, depending on the region). This was a noteworthy achievement -- Honor of Kings occupies the video game subgenre known as multiplayer online battle arena games (MOBAs), which are incomplete information games in the sense that players are unaware of the actions other players choose. The endgame, then, isn't merely AI that achieves Honor of Kings superhero performance, but insights that might be used to develop systems capable of solving some of society's toughest challenges. A paper published this week peels back the layers of Tencent's technique, which the coauthors describe as "highly scalable." They claim its novel strategies enable it to explore the game map "efficiently," with an actor-critic architecture that self-improves over time.

Procedural generation is a growing trend in game development. It allows developers to create games that are bigger and more dynamic, giving the games a higher level of replayability. Procedural generation isn't just one technique, it's a collection of techniques and approaches that are used together to create dynamic systems and objects. C is the industry-standard programming language to write computer games. It's at the heart of most engines, and is incredibly powerful.

Single-agent pathfinding on grid maps can exploit online compiled knowledge produced offline and saved as a Compressed Path Database (CPD). Such a knowledge is distilled by performing repeated searches in a graph, where each node corresponds to a distinct grid cell, typically by algorithms such as Dijkstra's. All-pairs shortest paths (APSPs) are computed and the first move along a shortest path is persistently stored in the CPD. This way, an optimal move can efficiently be retrieved for any pair of source and target cells that is considered while the agent is navigating. However, a CPD supports a static grid, that is, a grid where each cell is permanently either traversable or non-traversable. Our work instead assumes that the cells in the map can undergo dynamic changes. Reasoning about the altered map would require a new CPD. As creating it from scratch is computationally expensive, we present techniques to repair an existing CPD. We prove that using our technique leads to correct and optimal solutions. Experiments demonstrate the benefits of our approach. When a single obstacle of a given size is added or removed, the repair costs often are a small fraction of a recomputation from scratch.

In London last month, a team from Alphabet's UK-based artificial intelligence research unit DeepMind quietly laid a new marker in the contest between humans and computers. Thursday, it revealed the achievement, in a three-hour YouTube stream in which aliens and robots fought to the death. DeepMind's broadcast showed its artificial intelligence bot, AlphaStar, defeating a professional player at the complex real-time-strategy videogame StarCraft II. The machine-learning-powered software appeared to have discovered strategies unknown to the pros who compete for millions of dollars in prizes offered each year in one of e-sports' most lucrative games. "It was different from any StarCraft that I have played, Komincz, known professionally as MaNa, said Thursday.

The first thing to do when studying an algorithm is to understand the data. Input: Graph search algorithms, including A*, take a "graph" as input. A graph is a set of locations ("nodes") and the connections ("edges") between them. Here's the graph I gave to A*: A* doesn't see anything else. It only sees the graph.