Predicting User Perception of Move Brilliance in Chess

TL;DR

This paper introduces a neural network system that classifies chess moves as brilliant based on aesthetic appeal, achieving high accuracy and revealing insights into human perception of chess brilliance.

Contribution

It presents the first system for classifying brilliant chess moves, incorporating engine outputs and game tree features to model human aesthetic judgments.

Findings

The system achieves 79% accuracy in classifying brilliant moves.

Humans perceive moves as brilliant even if weaker engines consider them lower-quality.

The system enables computer chess to mimic human-like brilliance and creativity.

Abstract

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.