  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.
