Summary of Predicting User Perception Of Move Brilliance in Chess, by Kamron Zaidi and Michael Guerzhoy
Predicting User Perception of Move Brilliance in Chess
by Kamron Zaidi, Michael Guerzhoy
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel AI research paper in chess focuses on a previously unexplored aspect: aesthetic appeal. Specifically, it targets classifying “brilliant” moves, which are admired by players for their intellectual beauty. The proposed system uses a neural network, integrating chess engine output and game tree features. The accuracy is 79% (50% base-rate), PPV is 83%, and NPV is 75%. The study reveals that humans perceive “brilliant” moves as more than just the best possible move. A weaker engine’s assessment of a move as lower-quality can actually make it more likely to be predicted as brilliant by a stronger engine, all else being equal. This research opens up avenues for computer chess engines to demonstrate human-like creativity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new AI project in chess looks at something different: how beautiful the game is. It’s not just about winning or losing; it’s about making cool moves that players admire. The team created a system that can spot these “brilliant” moves by using special computer programs and analyzing the game tree. The system got 79% of the answers right, which is pretty good! This research shows that people don’t just look at what move is best; they also care about how clever or creative it is. This opens up new possibilities for computers to create beautiful chess moves, making them seem more human-like and creative. |
Keywords
» Artificial intelligence » Neural network
