超越游戏：神经蒙特卡洛树搜索应用的系统综述
Beyond Games: A Systematic Review of Neural Monte Carlo Tree Search Applications
AI总结：
本文系统综述了129篇神经蒙特卡洛树搜索在游戏外领域的应用，分析其结构与实践，旨在评估其成功能否扩展至其他领域，并为特定问题设计算法提供原则性指导。
摘要：
AlphaGo及其后继者的出现标志着使用人工智能进行游戏对弈的新范式开端。这是通过结合蒙特卡洛树搜索（一种规划过程）与深度学习实现的。虽然其对游戏领域的影响不可否认，但类似方法在游戏之外的应用中有多大用处，以及它们需要如何从原始方法中进行调整，则不太明确。我们回顾了129篇同行评审的文章，详细介绍了神经蒙特卡洛树搜索方法在游戏以外领域的应用。我们的目标是系统地评估此类方法在实践中的结构方式，以及其成功是否能够扩展到其他领域。我们发现了在多种领域的应用、许多利用学习到的策略和价值函数来引导树搜索的不同方式，以及各种训练方法。我们的综述描绘了神经蒙特卡洛树搜索家族算法在当前应用于实际问题时的景观，这是朝着为特定问题及其需求设计此类算法的更原则性方法迈出的第一步。
英文摘要：
The advent of AlphaGo and its successors marked the beginning of a new paradigm in playing games using artificial intelligence. This was achieved by combining Monte Carlo tree search, a planning procedure, and deep learning. While the impact on the domain of games has been undeniable, it is less clear how useful similar approaches are in applications beyond games and how they need to be adapted from the original methodology. We review 129 peer-reviewed articles detailing the application of neural Monte Carlo tree search methods in domains other than games. Our goal is to systematically assess how such methods are structured in practice and if their success can be extended to other domains. We find applications in a variety of domains, many distinct ways of guiding the tree search using learned policy and value functions, and various training methods. Our review maps the current landscape of algorithms in the family of neural monte carlo tree search as they are applied to practical problems, which is a first step towards a more principled way of designing such algorithms for specific problems and their requirements.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
