Procedural Content Generation (PCG) algorithms enable the automatic
generation of complex and diverse artifacts. However, they don't provide
high-level control over the generated content and typically require domain
expertise. In contrast, text-to-3D methods allow users to specify desired
characteristics in natural language, offering a high amount of flexibility and
expressivity. But unlike PCG, such approaches cannot guarantee functionality,
which is crucial for certain applications like game design. In this paper, we
present a method for generating functional 3D artifacts from free-form text
prompts in the open-world game Minecraft. Our method, DreamCraft, trains
quantized Neural Radiance Fields (NeRFs) to represent artifacts that, when
viewed in-game, match given text descriptions. We find that DreamCraft produces
more aligned in-game artifacts than a baseline that post-processes the output
of an unconstrained NeRF. Thanks to the quantized representation of the
environment, functional constraints can be integrated using specialized loss
terms. We show how this can be leveraged to generate 3D structures that match a
target distribution or obey certain adjacency rules over the block types.
DreamCraft inherits a high degree of expressivity and controllability from the
NeRF, while still being able to incorporate functional constraints through
domain-specific objectives.

本文介绍了一种从自由形式文本提示中生成功能性 3D 物品的方法，该方法使用量化的神经辐射场（NeRFs）来表示这些物品，以在游戏中与给定文本描述相匹配。

DreamCraft: 在 Minecraft 中基于文本指导生成具有功能的 3D 环境

DreamCraft: Text-Guided Generation of Functional 3D Environments in  Minecraft

Large Language Models (LLMs) are powerful tools, capable of leveraging their
training on natural language to write stories, generate code, and answer
questions. But can they generate functional video game levels? Game levels,
with their complex functional constraints and spatial relationships in more
than one dimension, are very different from the kinds of data an LLM typically
sees during training. Datasets of game levels are also hard to come by,
potentially taxing the abilities of these data-hungry models. We investigate
the use of LLMs to generate levels for the game Sokoban, finding that LLMs are
indeed capable of doing so, and that their performance scales dramatically with
dataset size. We also perform preliminary experiments on controlling LLM level
generators and discuss promising areas for future work.

本文探讨了使用大型语言模型 (LLMs) 生成 Sokoban 游戏关卡的可行性及效果，发现 LLMs 能够生成关卡，且其性能随数据集规模的提高而显著提高。同时，文章也介绍了控制 LLMs 关卡生成器的初步实验结果，并探讨了未来研究的有 promising 的方向。

大型语言模型生成游戏关卡

Level Generation Through Large Language Models

We study online convex optimization with constraints consisting of multiple
functional constraints and a relatively simple constraint set, such as a
Euclidean ball. As enforcing the constraints at each time step through
projections is computationally challenging in general, we allow decisions to
violate the functional constraints but aim to achieve a low regret and
cumulative violation of the constraints over a horizon of $T$ time steps.
First-order methods achieve an $\mathcal{O}(\sqrt{T})$ regret and an
$\mathcal{O}(1)$ constraint violation, which is the best-known bound under the
Slater's condition, but do not take into account the structural information of
the problem. Furthermore, the existing algorithms and analysis are limited to
Euclidean space. In this paper, we provide an \emph{instance-dependent} bound
for online convex optimization with complex constraints obtained by a novel
online primal-dual mirror-prox algorithm. Our instance-dependent regret is
quantified by the total gradient variation $V_*(T)$ in the sequence of loss
functions. The proposed algorithm works in \emph{general} normed spaces and
simultaneously achieves an $\mathcal{O}(\sqrt{V_*(T)})$ regret and an
$\mathcal{O}(1)$ constraint violation, which is never worse than the best-known
$( \mathcal{O}(\sqrt{T}), \mathcal{O}(1) )$ result and improves over previous
works that applied mirror-prox-type algorithms for this problem achieving
$\mathcal{O}(T^{2/3})$ regret and constraint violation. Finally, our algorithm
is computationally efficient, as it only performs mirror descent steps in each
iteration instead of solving a general Lagrangian minimization problem.

本文研究带复杂约束条件下在线凸优化问题，提出了一种基于镜像投影算法的新算法，可以在任何范数空间中实现低后悔和低约束违反度。

约束在线凸优化的梯度变差限制

Gradient-Variation Bound for Online Convex Optimization with Constraints

Bayesian neural network (BNN) priors are defined in parameter space, making
it hard to encode prior knowledge expressed in function space. We formulate a
prior that incorporates functional constraints about what the output can or
cannot be in regions of the input space. Output-Constrained BNNs (OC-BNN)
represent an interpretable approach of enforcing a range of constraints, fully
consistent with the Bayesian framework and amenable to black-box inference. We
demonstrate how OC-BNNs improve model robustness and prevent the prediction of
infeasible outputs in two real-world applications of healthcare and robotics.

提出 Output-Constrained BNN 技术，充分考虑贝叶斯框架下的函数约束，使用于 healthcare 和 robotics 应用中，改善模型鲁棒性和更好地防止不可行的预测输出。

输出受限贝叶斯神经网络

Output-Constrained Bayesian Neural Networks

The validity OF a causal model can be tested ONLY IF the model imposes
constraints ON the probability distribution that governs the generated data. IN
the presence OF unmeasured variables, causal models may impose two types OF
constraints : conditional independencies, AS READ through the d - separation
criterion, AND functional constraints, FOR which no general criterion IS
available.This paper offers a systematic way OF identifying functional
constraints AND, thus, facilitates the task OF testing causal models AS well AS
inferring such models FROM data.

本文提出了一种系统的方法来识别功能约束，从而有助于测试因果模型并从数据中推断出这样的模型。