Predicting User Perception of Move Brilliance in Chess
<|reference_start|>Predicting User Perception of Move Brilliance in Chess: 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.<|reference_end|>
@article{zaidi2024predicting,
    title={Predicting User Perception of Move Brilliance in Chess},
    author={Kamron Zaidi and Michael Guerzhoy},
    journal={Proc. of the International Conference for Computational Creativity
  (ICCC) 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.11895},
    primaryClass={cs.AI}
}