The Evolution of Chess AI

Advancements in artificial intelligence and deep learning have led to the rapid development of chess engines. What does the future for chess and AI hold?

Published on Oct. 31, 2022
The Evolution of Chess AI
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Chess is a two-player strategy board game played on a checkerboard with 64 squares, arranged in an 8×8 grid. Played by millions of people worldwide, chess is believed to be derived from the Indian game chaturanga sometime before the 7th century.

Chess has gained tremendous popularity, and it’s only growing as more people started playing during the global pandemic. There’s no better time to analyze the role of artificial intelligence in improving the quality of chess.

A Brief History of Chess AI

  • 1951: Alan Turing published the first program on paper theoretically capable of playing chess. 
  • 1989: Chess world champion Gary Kasparov defeated IBM’s Deep Thought in a chess match.
  • 1996: Kasparov defeated IBM’s Deep Blue in another match.
  • 1997: IBM’s Deep Blue becomes the first chess AI to defeat a grandmaster in a match.
  • 2017: AlphaZero, a neural net-based digital automaton, beats Stockfish 28–0, with 72 draws in chess matches.
  • 2019: Leela Chess Zero (LCZero v0.21.1-nT40.T8.610) defeats Stockfish 19050918 in a 100-game match 53.5 to 46.5 for the Top Chess Engine Championship season 15 title.
  • Present: Modern chess AI engines deploy deep learning to learn from thousands of matches. They regularly have FIDE ratings, chess’ rating system, above 3,400, far beyond the best human players. 

Artificial intelligence is a revolution in itself with numerous feats of accomplishments. The use of AI in the real world and real-life scenarios is ample. It has a wide array of use cases to improve the quality of life in general. Another wonderful use of artificial intelligence is in chess. 

We’ll first go over a brief introduction to the history of AI in chess. Then, we’ll learn about the modern evolution of chess engines and the influence of AI in the universe of chess. Finally, we’ll conclude with why every developer should try to implement the programming of a similar chess engine. Without further ado, let’s get started.

 

Chess AI History

In 1951, Alan Turing was the first to publish a program, developed on paper, that was capable of playing a full game of chess. Consistent developments were made in the upcoming years. New chess games and chess engines were developed during this time period. However, these AI chess engines had yet to achieve success on a higher level, possibly due to the lack of effective resources and tools.

In the 1980s, chess world champion Garry Kasparov made a strong claim that AI chess engines could never reach a level where they could defeat top-level chess grandmasters. His statement would remain true for a few years, as he successfully defended his throne in 1996 against IBM’s Deep Blue in a match over six games with 4:2. He had also defeated Deep Blue’s predecessor, IBM’s Deep Thought, in a 1989 clash. 

A year after losing to the world champion, Deep Blue came back to beat the world champion in a rematch. Kasparov was defeated by Deep Blue in the rematch with 2.5:3.5. Although there is a small controversy regarding the authority of this win, it’s mostly regarded in favor of the chess engine. In one of Kasparov’s recent interviews, he also agreed that he lost fair and square. 

Chess engines have come a long way from the era of the 1950s to the current generation of chess games. Let’s analyze the modern evolution of chess engines in the next section of this article to gain a better picture of the accomplishments made by AI in chess.

Game OnWhat Can We Learn From 4 Superhuman, Game-Playing AIs?

 

Modern Evolution Of Chess AI

Artificial intelligence algorithms developed for human play utilize many different principles. While it’s complicated to discuss what algorithm each engine uses for its functionality and workings, we know that a few popular engines like Alpha zero make use of neural networks, deep learning and neural net-like automation. Leela Chess Zero utilizes an open-source implementation of AlphaZero, which learns chess through self-play games and deep reinforcement learning.

Today, modern chess engines are so well-developed that they won’t drop a single game to human players. Even the current reigning world champion failed to beat the best modern chess engine once in a span of 100 games. The world champion has a FIDE rating of over 2800 across all formats. The contest usually takes place in a classical time format. The match was against Stockfish 9.

It has a rating of 3438 (the engine ratings are not FIDE ratings, but the player pool for engines is much stronger than for humans, so theoretically a FIDE rating for Stockfish 9 would be even higher). The result of the two modern clashes of chess engines are as follows, according to Wikipedia:

  • 2017: AlphaZero, a neural net-based digital automaton, beats Stockfish 28–0, with 72 draws, in a 100-game match.
  • 2019: Leela Chess Zero (LCZero v0.21.1-nT40.T8.610) defeats Stockfish 19050918 in a 100-game match 53.5 to 46.5 for the TCEC season 15 title.

These results, coupled with the rise of chess engines and neural networks and deep learning-based chess networks taking over the chess world by storm, are a huge sign of the potential for greater and more enormous possibilities. Let’s delve into the potential influence of AI in the universe of chess.

 

How Chess AI Has Influenced Chess

Artificial intelligence has influenced the way chess games are played at the top level. Most of the grandmasters and super grandmasters (rated at a FIDE above 2700) utilize modern AI chess engines to analyze their games, as well as the games of their competitors. There’s a complete turnaround in the way in which chess games are now played.

The basic opening theories and other analytical concepts are thoroughly analyzed. In classical formats of chess, you’ll typically see these high-level players make about 10-to-15 of their first moves from previously analyzed games or the top engine recommendations.

The quality of top-level games has also drastically improved because of the help of these engines. It’s almost impossible to rate or compare a modern player against a legendary player from past decades thanks to the enormous improvement these chess engines have provided.

Some have argued that chess engines have had a negative effect on the game because it’s more about theory than actual practice and play. Others argue that the influence of AI on chess has led to a  drastic improvement in competition and further advancements are yet to be made to challenge modern players.

And ultimately they make these players much better both with opening theory and other tricks up their sleeves. There is still room for human error. These errors can be exploited by players to gain advantages and create interesting games of attacking chess.

More on AI4 Reasons Why Deep Learning and Neural Networks Aren’t Always the Right Choice

 

Advantages of Building Your Own Chess AI

No matter what level of programmer you are, you will have a great time exploring the numerous aspects of building your own chess engine. I firmly believe that programmers and developers should try to develop a game with Python and AI. 

If you’re a beginner, then you can try to explore the artistic features while implementing the structure of the board and the pieces. You will learn the features of functions and classes to implement the structure of the board, as well as place the pieces in their respective positions. You can experiment with the two colors of the chessboard and the chess pieces. Finally, you will gain a cool graphical structure that you’ll design from scratch using your coding skills.

If you are an intermediate AI developer, you can start implementing the functionalities of the pieces and their respective movements. Each piece has its own representation and notation for the particular motion. You need to implement these positions and also compute the capturing of each of these pieces. All this work does not have to be perfect to develop the skills and get a working program.

Finally, for advanced programmers and AI developers, you can study a bunch of games and chess engines that have been developed in recent years. Using the data available, you can construct deep learning neural networks for your chess engine. The chess engine can learn from games played since the 1800s. Chess has a huge data set, allowing developers to create some quality chess engines from scratch by themselves.

Artificial intelligence is a revolutionary phenomenon, and it has undoubtedly changed the landscape of chess. The impact AI has had on chess can be argued with both pros and cons.

However, it’s undeniable that the influence, the hype and the excitement generated by AI is magnificent. Artificial intelligence has great potential in the field of chess. Modern developments and advancements in chess engines are increasing rapidly. We can have much greater feats of accomplishments in the modern world of chess.

I am excited to see where these improvements reach. I also hope all of you will try to implement a chessboard or a chess engine of your own as a fun project.

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John Deere
Artificial Intelligence • Cloud • Internet of Things • Machine Learning • Analytics • Industrial
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