Real-Time Strategy (RTS) game unit generation is an unexplored area of Procedural Content Generation (PCG) research, which leaves the question of how to automatically generate interesting and balanced units unanswered. Creating unique and balanced units can be a difficult task when designing an RTS game, even for humans. Having an automated method of designing units could help developers speed up the creation process as well as find new ideas. In this work we propose a method of generating balanced and useful RTS units. We draw on Search-Based PCG and a fitness function based on Monte Carlo Tree Search (MCTS). We present ten units generated by our system designed to be used in the game microRTS, as well as results demonstrating that these units are unique, useful, and balanced.

Player modelling is the field of study associated with understanding players. One pursuit in this field is affect prediction: the ability to predict how a game will make a player feel. We present novel improvements to affect prediction by using a deep convolutional neural network (CNN) to predict player experience trained on game event logs in tandem with localized level structure information. We test our approach on levels based on Super Mario Bros. (Infinite Mario Bros.) and Super Mario Bros.: The Lost Levels (Gwario), as well as original Super Mario Bros. levels. We outperform prior work, and demonstrate the utility of training on player logs, even when lacking them at test time for cross-domain player modelling.

In fighting games, individual players of the same skill level often exhibit distinct strategies from one another through their gameplay. Despite this, the majority of AI agents for fighting games have only a single strategy for each "level" of difficulty. To make AI opponents more human-like, we'd ideally like to see multiple different strategies at each level of difficulty, a concept we refer to as "multidimensional" difficulty. In this paper, we introduce a diversity-based deep reinforcement learning approach for generating a set of agents of similar difficulty that utilize diverse strategies. We find this approach outperforms a baseline trained with specialized, human-authored reward functions in both diversity and performance.

可解释的人工智能（XAI）方法旨在帮助人类用户更好地了解AI代理的决策。但是，许多现代的XAI方法对最终用户，尤其是那些没有先前AI或ML知识的用户都不纯粹。在本文中，我们提出了一种新颖的XAI方法，我们称为责任，标识了特定决定的最负责任的培训示例。然后可以将此示例显示为一个解释：“这是我（AI）学到的使我做到的。”我们介绍了许多领域的实验结果，以及亚马逊机械Turk用户研究的结果，比较了责任和图像分类任务上的现有XAI方法。我们的结果表明，责任可以帮助提高人类最终用户和次要ML模型的准确性。

2D动画是游戏开发的常见因素，用于角色，效果和背景艺术。它涉及需要技巧和时间的工作，但其中一部分是重复的和乏味的。存在自动动画方法，但设计的是没有动画师的设计。重点是现实生活中的视频，该视频遵循对象如何移动的严格定律，并且不考虑2D动画中经常存在的风格运动。我们提出了一个问题公式，更紧密地遵守动画的标准工作流程。我们还展示了一个模型，即Schrotchbetween，该模型学会了在钥匙帧之间映射，并在速率内绘制了绘制的精灵动画。我们证明我们的问题公式为任务提供了所需的信息，并且我们的模型优于现有方法。

机器学习在图像处理方面取得了很大的成功。但是，这项工作的重点很大程度上是在逼真的图像上，忽略了更多的小众艺术风格，例如像素艺术。此外，许多专注于像素组的传统机器学习模型与单个像素很重要的像素艺术无法很好地工作。我们提出了一个专门的VQ-VAE模型Pixel VQ-VAE，该模型学习了Pixel Art的表示。我们表明，它在嵌入质量以及下游任务的性能中都优于其他模型。

Extracting complex structures from grid-based data is a common key step in automated medical image analysis. The conventional solution to recovering tree-structured geometries typically involves computing the minimal cost path through intermediate representations derived from segmentation masks. However, this methodology has significant limitations in the context of projective imaging of tree-structured 3D anatomical data such as coronary arteries, since there are often overlapping branches in the 2D projection. In this work, we propose a novel approach to predicting tree connectivity structure which reformulates the task as an optimization problem over individual steps of a recursive process. We design and train a two-stage model which leverages the UNet and Transformer architectures and introduces an image-based prompting technique. Our proposed method achieves compelling results on a pair of synthetic datasets, and outperforms a shortest-path baseline.

There are multiple scales of abstraction from which we can describe the same image, depending on whether we are focusing on fine-grained details or a more global attribute of the image. In brain mapping, learning to automatically parse images to build representations of both small-scale features (e.g., the presence of cells or blood vessels) and global properties of an image (e.g., which brain region the image comes from) is a crucial and open challenge. However, most existing datasets and benchmarks for neuroanatomy consider only a single downstream task at a time. To bridge this gap, we introduce a new dataset, annotations, and multiple downstream tasks that provide diverse ways to readout information about brain structure and architecture from the same image. Our multi-task neuroimaging benchmark (MTNeuro) is built on volumetric, micrometer-resolution X-ray microtomography images spanning a large thalamocortical section of mouse brain, encompassing multiple cortical and subcortical regions. We generated a number of different prediction challenges and evaluated several supervised and self-supervised models for brain-region prediction and pixel-level semantic segmentation of microstructures. Our experiments not only highlight the rich heterogeneity of this dataset, but also provide insights into how self-supervised approaches can be used to learn representations that capture multiple attributes of a single image and perform well on a variety of downstream tasks. Datasets, code, and pre-trained baseline models are provided at: https://mtneuro.github.io/ .

Cohn and Umans proposed a framework for developing fast matrix multiplication algorithms based on the embedding computation in certain groups algebras. In subsequent work with Kleinberg and Szegedy, they connected this to the search for combinatorial objects called strong uniquely solvable puzzles (strong USPs). We begin a systematic computer-aided search for these objects. We develop and implement constraint-based algorithms build on reductions to $\mathrm{SAT}$ and $\mathrm{IP}$ to verify that puzzles are strong USPs, and to search for large strong USPs. We produce tight bounds on the maximum size of a strong USP for width $k \le 5$, construct puzzles of small width that are larger than previous work, and improve the upper bounds on strong USP size for $k \le 12$. Although our work only deals with puzzles of small-constant width, the strong USPs we find imply matrix multiplication algorithms that run in $O(n^\omega)$ time with exponent $\omega \le 2.66$. While our algorithms do not beat the fastest algorithms, our work provides evidence and, perhaps, a path to finding families of strong USPs that imply matrix multiplication algorithms that are more efficient than those currently known.

Agile robotics presents a difficult challenge with robots moving at high speeds requiring precise and low-latency sensing and control. Creating agile motion that accomplishes the task at hand while being safe to execute is a key requirement for agile robots to gain human trust. This requires designing new approaches that are flexible and maintain knowledge over world constraints. In this paper, we consider the problem of building a flexible and adaptive controller for a challenging agile mobile manipulation task of hitting ground strokes on a wheelchair tennis robot. We propose and evaluate an extension to work done on learning striking behaviors using a probabilistic movement primitive (ProMP) framework by (1) demonstrating the safe execution of learned primitives on an agile mobile manipulator setup, and (2) proposing an online primitive refinement procedure that utilizes evaluative feedback from humans on the executed trajectories.