In this paper, we introduce an innovative deep learning architecture, termed
Xiangqi Structurally Variable (XQSV), designed to emulate the behavioral
patterns of human players in Xiangqi, or Chinese Chess. The unique attribute of
XQSV is its capacity to alter its structural configuration dynamically,
optimizing performance for the task based on the particular subset of data on
which it is trained. We have incorporated several design improvements to
significantly enhance the network's predictive accuracy, including a local
illegal move filter, an Elo range partitioning, a sequential one-dimensional
input, and a simulation of imperfect memory capacity. Empirical evaluations
reveal that XQSV attains a predictive accuracy of approximately 40%, with its
performance peaking within the trained Elo range. This indicates the model's
success in mimicking the play behavior of individuals within that specific
range. A three-terminal Turing Test was employed to demonstrate that the XQSV
model imitates human behavior more accurately than conventional Xiangqi
engines, rendering it indistinguishable from actual human opponents. Given the
inherent nondeterminism in human gameplay, we propose two supplementary relaxed
evaluation metrics. To our knowledge, XQSV represents the first model to mimic
Xiangqi players.

介绍了一种创新的深度学习架构 XQSV，它模拟了中国象棋中人类玩家的行为模式。通过动态改变结构配置，XQSV 能够根据其训练数据的特定子集来优化性能。实证评估表明，XQSV 在训练 Elo 范围内具有约 40% 的预测准确率，并成功模拟了该特定范围内个体的游戏行为。采用三终端图灵测试证明，与传统象棋引擎相比，XQSV 模型更准确地模仿了人类行为，几乎不可区分。据我们所知，XQSV 是第一个模仿象棋玩家的模型。

XQSV：一种变结构网络模拟中国象棋中人类对弈

XQSV: A Structurally Variable Network to Imitate Human Play in Xiangqi

This paper presents an empirical exploration of non-transitivity in
perfect-information games, specifically focusing on Xiangqi, a traditional
Chinese board game comparable in game-tree complexity to chess and shogi. By
analyzing over 10,000 records of human Xiangqi play, we highlight the existence
of both transitive and non-transitive elements within the game's strategic
structure. To address non-transitivity, we introduce the JiangJun algorithm, an
innovative combination of Monte-Carlo Tree Search (MCTS) and Policy Space
Response Oracles (PSRO) designed to approximate a Nash equilibrium. We evaluate
the algorithm empirically using a WeChat mini program and achieve a Master
level with a 99.41\% win rate against human players. The algorithm's
effectiveness in overcoming non-transitivity is confirmed by a plethora of
metrics, such as relative population performance and visualization results. Our
project site is available at
https://sites.google.com/view/jiangjun-site/.

通过分析超过 10,000 条人类象棋比赛记录，本文重点研究了象棋游戏中存在的传递性与非传递性元素，并引入了 JiangJun 算法，这是一种蒙特卡洛树搜索（MCTS）和策略空间响应神谕（PSRO）的创新组合，旨在近似纳什均衡。我们通过一个微信小程序对算法进行了经验评估，在与人类玩家的比赛中取得了 99.41％的胜率，达到大师级水平。量化指标如相对人群表现和可视化结果证实了该算法在克服非传递性方面的有效性。