Games, including abstract board games, constitute a convenient ground to
create, design, and improve new AI methods. In this field, Monte Carlo Tree
Search is a popular algorithm family, aiming to build game trees and explore
them efficiently. Combinatorial Optimization, on the other hand, aims to model
and solve problems with an objective to optimize and constraints to satisfy,
and is less common in Game AI. We believe however that both methods can be
combined efficiently, by injecting Combinatorial Optimization into Monte Carlo
Tree Search to help the tree search, leading to a novel combination of these
two techniques. Tested on the board game boop., our method beats 96% of the
time the Monte Carlo Tree Search algorithm baseline. We conducted an ablation
study to isolate and analyze which injections and combinations of injections
lead to such performances. Finally, we opposed our AI method against human
players on the Board Game Arena platform, and reached a 373 ELO rating after 51
boop. games, with a 69% win rate and finishing ranked 56th worldwide on the
platform over 5,316 boop. players.

通过将组合优化注入蒙特卡罗树搜索，我们在抽象棋盘游戏中提出了一种新的 AI 方法，该方法在对强化学习算法的基线进行测试时的胜率达到 96％，并在 Board Game Arena 平台上与人类玩家对抗时取得了良好的成绩。

将组合优化引入 MCTS 方法：应用于棋盘游戏 boop

Injecting Combinatorial Optimization into MCTS: Application to the Board  Game boop

The ability to detect macroscopic changes is important for probing the
behaviors of experimental many-body systems from the classical to the quantum
realm. Although abrupt changes near phase boundaries can easily be detected,
subtle macroscopic changes are much more difficult to detect as the changes can
be obscured by noise. In this study, as a toy model for detecting subtle
macroscopic changes in many-body systems, we try to differentiate scalar field
samples at varying temperatures. We compare different methods for making such
differentiations, from physics method, statistics method, to AI method. Our
finding suggests that the AI method outperforms both the statistical method and
the physics method in its sensitivity. Our result provides a proof-of-concept
that AI can potentially detect macroscopic changes in many-body systems that
elude physical measures.

AI 方法在探测难以通过物理测量捕捉到的多体系统的宏观变化方面表现优于统计方法和物理方法，从而提供了 AI 方法能够检测宏观变化的概念验证。

利用机器学习检测有限温度经典标量场中微小的宏观变化

Detecting subtle macroscopic changes in a finite temperature classical  scalar field with machine learning

Given an on-board diagnostics (OBD) dataset and a physics-based emissions
prediction model, this paper aims to develop an accurate and
computational-efficient AI (Artificial Intelligence) method that predicts
vehicle emissions. The problem is of societal importance because vehicular
emissions lead to climate change and impact human health. This problem is
challenging because the OBD data does not contain enough parameters needed by
high-order physics models. Conversely, related work has shown that low-order
physics models have poor predictive accuracy when using available OBD data.
This paper uses a divergent window co-occurrence pattern detection method to
develop a spatiotemporal variability-aware AI model for predicting emission
values from the OBD datasets. We conducted a case study using real-world OBD
data from a local public transportation agency. Results show that the proposed
AI method has approximately 65% improved predictive accuracy than a non-AI
low-order physics model and is approximately 35% more accurate than a baseline
model.

本文利用 OBD 数据集和基于物理的排放预测模型，旨在开发一种准确且计算效率高的人工智能方法来预测车辆排放，实证结果表明，相较于低阶物理模型和基础模型，提出的人工智能方法可使预测准确率提高 65％和 35％。