How the AI Revolution Impacted Chess (1/2)

by Joshua Doknjas
1/7/2022 – In 2017, AlphaZero shocked the chess world by crushing Stockfish in a 100-game match. Since then, chess engines have undergone a substantial revision based on deep learning methods to develop a neural network. Joshua Doknjas examines the impact of the AI revolution in chess. | Graphic: Europe Echecs

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The wave of neural network engines that AlphaZero inspired have impacted chess preparation, opening theory, and middlegame concepts. We can see this impact most clearly at the elite level because top grandmasters prepare openings and get ideas by working with modern engines. For instance, Carlsen cited AlphaZero as a source of inspiration for his remarkable play in 2019.

Neural network engines like AlphaZero learn from experience by developing patterns through numerous games against itself (known as self-play reinforcement learning) and understanding which ideas work well in different types of positions. This pattern recognition ability suggests that they are especially strong in openings and strategic middlegames where long-term factors must be assessed accurately. In these areas of chess, their experience allows them to steer the game towards positions that provide relatively high probabilities of winning.

A table of four selected engines is provided below.

Chess Engines

Engine

Type

Description

Stockfish 8

Classical

Relies on hard-wired rules and brute-force calculation of variations.

AlphaZero

Neural network

DeepMind’s revolutionary AI engine used self-play reinforcement learning to train a neural network.

Leela Chess Zero (Lc0)

Neural network

Launched in 2018 as an open-source project to follow the footsteps of AlphaZero.

Stockfish 12

(and newer versions)

Hybrid

Utilizes classical searching algorithms as well as a neural network.

The hybrid Stockfish engine aims to get the best of both types of AI: the calculation speed of classical engines and the strategic understanding of neural networks. Practice has shown that this approach is a very effective one because it consistently evaluates all types of positions accurately, from strategic middlegames to messy complications.

These two articles introduce a few concepts that the newer (i.e., neural network and hybrid) engines have influenced. Please note that the game annotations are based on work I did for my book, The AI Revolution in Chess, where I analyzed the impact of AI engines.

Clash of Styles

One of the biggest differences in understanding between older and newer engines can be found in strategic middlegames which involve long-term improvements by one side. As shown in many of the AlphaZero – Stockfish games, the older engines sometimes fail to see dangers due to their limited foresight. Relying solely on move-by-move calculation is not always enough to solve problems against the strongest opponents. This is because neural network engines excel at slowly building up pressure, making small improvements to optimize their winning chances, before gradually preparing the decisive breakthrough.

In the following game, the older engines believe that the opening outcome is quite satisfactory for Black, while the newer ones strongly disagree. Grischuk sides with the opinion of the neural network engines and understands that White’s long-term initiative is both practically and objectively extremely difficult for Black to handle.

 

Opening Developments

Perhaps the most popularized idea of the neural network engines is the h-pawn advance, where White pushes h4-h5-h6 (or Black pushes …h5-h4-h3) to cramp the opponent’s kingside by taking away some key squares. The idea itself is not at all new, but the newer engines have a much greater appreciation for it than the older ones. This has led to many new ideas in openings such as the Grunfeld, where the fianchettoed bishop on g7 can be targeted by an h-pawn attack. Tying back to the theme of long-term improvements, neural network engines understand the problems that it creates for the opponent in the long run.

Our next game surveys a cutting-edge approach against the Grunfeld. Its sharp rise in popularity from 2019 onwards coincides with the widespread use of neural network engines at the top level.

 

The clash of chess styles between classical and neural network AI is fascinating to analyze. Many examples on this topic can be found in the famous AlphaZero – Stockfish games and in openings where the engines disagree on the evaluation, such as the Grischuk – Nakamura game. Their disagreement has led to major advancements in all popular openings, as old lines are revised, and new lines supported by modern engines are introduced into high-level practice.

Part 2 will examine another AI-inspired opening and the modern battle between two players armed with ideas from neural network engines.

Links


Joshua Doknjas is a FIDE Master from Canada and the author of two books on the Sicilian Najdorf and Ruy Lopez. He enjoys teaching, following, and writing about chess. Joshua is especially interested in the role of engines during opening preparation and understanding how AI has influenced modern chess.
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HolaAmigo HolaAmigo 1/8/2022 10:22
Hard to understand when you do not get what NN, remisses and all the rest mean. Looks like it could be interesting.
I love chess, and imo all these engines are like buldozzers carving highways in the Brazil jungle. They kill the jungle. Short of natural intelligence, let's go for AI
physica physica 1/8/2022 04:35
The search of A0 (and Lc0) (Monte Carlo Method) was invented by John von Neumann and Stanislaw Ulam during World War II [source: IBM]. The origin of Minimax algorithm, the basis of Alpha-beta pruning (invented during the 1950s) is controversial but safely in early 1900s. So the term 'revolution' is a bit silly because computer chess has been taking giant leaps in the last 5, 10, 20 years... basically from day zero. NN engines didn't reinvent chess, nor would they be at the level without decades of human work paved for them. To put it more offensively, the level was so incredibly high before NNs that no human can reproduce it over the board. This leads to a question, what are we here actually arguing about: Classical chess engines (HCE, hard coded evaluation) vs. NNs or Humans + NNs vs. HCE engines? I'm confused. Why are NN engines better than HCE? Simply because of NN evaluation black box, not search.

I don't see much point to argue when human play is mixed with contemporary engine level. Carlsen not playing 30-move remisses to become the only dominating player of 2010s is not due to NNs or engines at all, more like natural talent and skill. It's like a being chess fan seeing a player following engine lines and crushing his opponent. You want to ask what engine did he use, what GUI etc. all irrelevant details imo. Respectively, how/why did the opponent lose? -Simply by blundering. Why did he/she blunder? Again, the details are irrelevant.
OmNamaShivaYa OmNamaShivaYa 1/8/2022 04:03
I've been pushing the H pawn against the fianchettoed bishop for years.
dkln dkln 1/8/2022 10:38
The book by Doknjas is very interesting, as it gives a rather complete analysis how the AI revolution changed the game; especially the different phases of the game. Highly recommended.

Shameless self-plug: If you want to know more about the technical _inner_ workings of such AI based engines, you can take a look at my free book at github.com/asdfjkl/neural_network_chess
math_lover math_lover 1/8/2022 07:48
What is AI
there is nothing like that in carabian Islands
Dan Durham Dan Durham 1/8/2022 05:32
I'm working on the ultimate chess program right now utilizing the resurrected neurons of past chess greats such as Capablanca, Fischer, and Morphy. I'm calling it Alpha Infinity.
Werewolf Werewolf 1/7/2022 10:59
You haven’t mentioned at all the search method of Alpha Zero / Lc0. A network without a search isn’t much good.

Also SF search might be technically called “Brute Force” but in reality it’s very heavily pruned.
sedarpl sedarpl 1/7/2022 07:15
Good article, thx:-)
WillScarlett WillScarlett 1/7/2022 06:58
Permit me an off-topic digression, please. At this exact moment this is the fifth and top article for January 7th, and it's almost 7:00 PM in Germany. You might know that today is the anniversary of the birth date, in 1916, of the legendary Estonian GM Paul Keres ( who was stronger than a number of world champions ).

Maybe it's not too late for a respectful and appreciative article - devoid of commercial content.
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