mlsanityData to 14 Oct 2026

Chess AI: Competing Paradigms for Machine Intelligence

arXiv 2109.11602 ↗First posted 23 Sep 2021cs.AI cs.LG stat.MLcited by 8

Not on the map. Why?

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.

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

13 of 69 references.

References · 13

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
  4. 2015Distilling the Knowledge in a Neural Networkcited by 10,324
  5. 2017Squeeze-and-Excitation Networkscited by 4,789
  6. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
  7. 2015Learning both Weights and Connections for Efficient Neural Networkscited by 2,031
  8. 2017Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inferencecited by 1,593
  9. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,353
  10. 2018A Game-Based Approximate Verification of Deep Neural Networks with Provable Guaranteescited by 33
  11. 2017Towards "AlphaChem": Chemical Synthesis Planning with Tree Search and Deep Neural Network Policiescited by 5
  12. 2014A Bellman View of Jesse Livermorecited by 3
1 more
  1. 2015The Digital Synaptic Neural Substrate: A New Approach to Computational Creativitycited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 2; 2023: 2; 2024: 1; 2025: 1; 2026: 2.

8 papers.

  1. 2023Adaptive Online Replanning with Diffusion Modelscited by 34
  2. 2024Designing Skill-Compatible AI: Methodologies and Frameworks in Chesscited by 10
  3. 2022Learning Chess With Language Models and Transformerscited by 6
  4. 2022Impartial Games: A Challenge for Reinforcement Learningcited by 3
  5. 2023The Value of Chess Squarescited by 2
  6. 2026Mixture of Masters: Sparse Chess Language Models with Player Routingcited by 1
  7. 2025Liquid Reasoning Transformers: A Sudoku-Based Prototype for Chess-Scale Algorithmic Taskscited by 0
  8. 2026Abstract Concept Modelling in Conceptual Spaces: A Study on Chess Strategiescited by 0