Predicting User Perception of Move Brilliance in Chess

• URL: http://arxiv.org/abs/2406.11895v1
• Date: Fri, 14 Jun 2024 17:46:26 GMT
• Title: Predicting User Perception of Move Brilliance in Chess
• Authors: Kamron Zaidi, Michael Guerzhoy,
• Abstract summary: We show the first system for classifying chess moves as brilliant. The system achieves an accuracy of 79% (with 50% base-rate), a PPV of 83%, and an NPV of 75%. 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.
• Score: 3.434553688053531
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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