AI research in chess has been primarily focused on producing stronger agents
that can maximize the probability of winning. However, there is another aspect
to chess that has largely gone unexamined: its aesthetic appeal. Specifically,
there exists a category of chess moves called ``brilliant" moves. These moves
are appreciated and admired by players for their high intellectual aesthetics.
We demonstrate the first system for classifying chess moves as brilliant. The
system uses a neural network, using the output of a chess engine as well as
features that describe the shape of the game tree. The system achieves an
accuracy of 79% (with 50% base-rate), a PPV of 83%, and an NPV of 75%. We
demonstrate that what humans perceive as ``brilliant" moves is not merely the
best possible move. We show that a move is more likely to be predicted as
brilliant, all things being equal, if a weaker engine considers it
lower-quality (for the same rating by a stronger engine). Our system opens the
avenues for computer chess engines to (appear to) display human-like
brilliance, and, hence, creativity.

研究论文说明了如何使用神经网络和棋局形状特征对象标准为 “卓越” 的棋局进行分类，达到了 79％的准确性，揭示了较弱引擎对棋局评价较低时更有可能被预测为 “卓越” 的特点，引领了计算机国际象棋引擎展示类似人类卓越和创意的可能性。