# Chess AI: Competing Paradigms for Machine Intelligence

arXiv: https://arxiv.org/abs/2109.11602 · HTML: https://mlsanity.com/paper/2109.11602/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 69 references that are in the map.
- First posted on arXiv 23 Sep 2021.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning both Weights and Connections for Efficient Neural Networks](https://mlsanity.com/paper/1506.02626/) (2015)
- [Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference](https://mlsanity.com/paper/1712.05877/) (2017)
- [Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model](https://mlsanity.com/paper/1911.08265/) (2019)
- [A Game-Based Approximate Verification of Deep Neural Networks with Provable Guarantees](https://mlsanity.com/paper/1807.03571/) (2018)
- [Towards "AlphaChem": Chemical Synthesis Planning with Tree Search and Deep Neural Network Policies](https://mlsanity.com/paper/1702.00020/) (2017)
- [A Bellman View of Jesse Livermore](https://mlsanity.com/paper/1407.2642/) (2014)
- [The Digital Synaptic Neural Substrate: A New Approach to Computational Creativity](https://mlsanity.com/paper/1507.07058/) (2015)

## Built on by (most cited first, 8 of 8)

- [Adaptive Online Replanning with Diffusion Models](https://mlsanity.com/paper/2310.09629/) (2023)
- [Designing Skill-Compatible AI: Methodologies and Frameworks in Chess](https://mlsanity.com/paper/2405.05066/) (2024)
- [Learning Chess With Language Models and Transformers](https://mlsanity.com/paper/2209.11902/) (2022)
- [Impartial Games: A Challenge for Reinforcement Learning](https://mlsanity.com/paper/2205.12787/) (2022)
- [The Value of Chess Squares](https://mlsanity.com/paper/2307.05330/) (2023)
- [Mixture of Masters: Sparse Chess Language Models with Player Routing](https://mlsanity.com/paper/2602.04447/) (2026)
- [Liquid Reasoning Transformers: A Sudoku-Based Prototype for Chess-Scale Algorithmic Tasks](https://mlsanity.com/paper/2512.12792/) (2025)
- [Abstract Concept Modelling in Conceptual Spaces: A Study on Chess Strategies](https://mlsanity.com/paper/2601.21771/) (2026)

## Abstract (arXiv)

Endgame studies have long served as a tool for testing human creativity and intelligence. We find that they can serve as a tool for testing machine ability as well. Two of the leading chess engines, Stockfish and Leela Chess Zero (LCZero), employ significantly different methods during play. We use Plaskett's Puzzle, a famous endgame study from the late 1970s, to compare the two engines. Our experiments show that Stockfish outperforms LCZero on the puzzle. We examine the algorithmic differences between the engines and use our observations as a basis for carefully interpreting the test results. Drawing inspiration from how humans solve chess problems, we ask whether machines can possess a form of imagination. On the theoretical side, we describe how Bellman's equation may be applied to optimize the probability of winning. To conclude, we discuss the implications of our work on artificial intelligence (AI) and artificial general intelligence (AGI), suggesting possible avenues for future research.