Evaluating In Silico Creativity: An Expert Review of AI Chess Compositions

Vivek Veeriah, Federico Barbero, Marcus Chiam, Xidong Feng, Michael D Dennis, Ryan Pachauri, Thomas Tumiel, Johan Obando-Ceron, Jiaxin Shi, Shaobo Hou, Satinder Singh, Nenad Tomasev, Tom Zahavy

Track: Paper

Keywords: Generative AI, Computational Creativity, Creativity, Aesthetics, Novelty, Chess Puzzles

TL;DR: We developed an AI that generates creative chess puzzles. World-renowned experts rated them as beautiful, enjoyable, and counter-intuitive, with some puzzles approaching the quality of classic compositions.

Abstract: 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 outline our methodology, with a detailed discussion in the technical paper. 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. This paper compiles these selected puzzles, integrating expert analysis to explore the elements that render them counter-intuitive and beautiful.

Submission Number: 26