In our work, we continue to explore the properties of interval-valued fuzzy
soft sets, which are obtained by combining interval-valued fuzzy sets and soft
sets. We introduce the concept of energy of an interval-valued fuzzy soft set,
as well as pessimistic and optimistic energy, enabling us to construct an
effective decision-making algorithm. Through examples, the paper demonstrates
how the introduced algorithm is successfully applied to problems involving
uncertainty. Additionally, we compare the introduced method with other methods
dealing with similar or related issues.

我们的工作继续探讨了区间值模糊软集的特性，该特性是通过组合区间值模糊集和软集得到的。我们引入了区间值模糊软集的能量概念，以及悲观和乐观能量，从而构建了一个有效的决策算法。通过例子，本文展示了该算法如何成功应用于涉及不确定性的问题。此外，我们将引入的方法与其他处理类似或相关问题的方法进行了比较。

基于区间值模糊软集能量的决策算法

Decision-making algorithm based on the energy of interval-valued fuzzy  soft sets

In this study, we explore the efficiency of the Monte Carlo Tree Search
(MCTS), a prominent decision-making algorithm renowned for its effectiveness in
complex decision environments, contingent upon the volume of simulations
conducted. Notwithstanding its broad applicability, the algorithm's performance
can be adversely impacted in certain scenarios, particularly within the domain
of game strategy development. This research posits that the inherent branch
divergence within the Da Vinci Code board game significantly impedes
parallelism when executed on Graphics Processing Units (GPUs). To investigate
this hypothesis, we implemented and meticulously evaluated two variants of the
MCTS algorithm, specifically designed to assess the impact of branch divergence
on computational performance. Our comparative analysis reveals a linear
improvement in performance with the CPU-based implementation, in stark contrast
to the GPU implementation, which exhibits a non-linear enhancement pattern and
discernible performance troughs. These findings contribute to a deeper
understanding of the MCTS algorithm's behavior in divergent branch scenarios,
highlighting critical considerations for optimizing game strategy algorithms on
parallel computing architectures.

通过比较分析基于 CPU 和 GPU 的 MCTS 算法在分支分歧情况下的性能，我们发现 CPU 实现呈线性改进趋势，而 GPU 实现呈现出非线性的增强模式和明显的性能下降。这些发现对于优化并行计算架构上的游戏策略算法具有重要的考虑价值。

达芬奇密码游戏策略模拟的蒙特卡罗树搜索算法的开发与应用

Development and Application of a Monte Carlo Tree Search Algorithm for  Simulating Da Vinci Code Game Strategies

Navigating urban environments represents a complex task for automated
vehicles. They must reach their goal safely and efficiently while considering a
multitude of traffic participants. We propose a modular decision making
algorithm to autonomously navigate intersections, addressing challenges of
existing rule-based and reinforcement learning (RL) approaches. We first
present a safe RL algorithm relying on a model-checker to ensure safety
guarantees. To make the decision strategy robust to perception errors and
occlusions, we introduce a belief update technique using a learning based
approach. Finally, we use a scene decomposition approach to scale our algorithm
to environments with multiple traffic participants. We empirically demonstrate
that our algorithm outperforms rule-based methods and reinforcement learning
techniques on a complex intersection scenario.

我们提出了一种基于模块化的决策算法，以自主导航十字路口的挑战，通过安全机制，置信度更新技术和场景分解方法，提高了行车安全性和决策鲁棒性，并在复杂的十字路口情境中表现了优越性。