Evaluating In Silico Creativity: An Expert Review of AI Chess Compositions
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Abstract (arXiv)
The rapid advancement of Generative AI has raised significant questions regarding its ability to produce creative and novel outputs. Our recent work investigates this question within the domain of chess puzzles and presents an AI system designed to generate puzzles characterized by aesthetic appeal, novelty, counter-intuitive and unique solutions. We briefly discuss our method below and refer the reader to the technical paper for more details. To assess our system's creativity, we presented a curated booklet of AI-generated puzzles to three world-renowned experts: International Master for chess compositions Amatzia Avni, Grandmaster Jonathan Levitt, and Grandmaster Matthew Sadler. All three are noted authors on chess aesthetics and the evolving role of computers in the game. They were asked to select their favorites and explain what made them appealing, considering qualities such as their creativity, level of challenge, or aesthetic design.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 0 of its 1 references that are in the map. First posted on arXiv 27 Oct 2025.
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Citing papers per year: 2026: 3.
3 papers.
- 2026Conditional Generation of Creative Chess Puzzles with Diffusion Models29 Sep 2026
- 2026COrigami: An AI Pipeline for Co-Designing Flat-Foldable Visually Recognisable Origami24 Jun 2026
- 2026VAM: Verbalized Action Masking for Controllable Exploration in RL Post-Training -- A Chess Case Study18 Feb 2026