How Chess Patterns Matter to Making a Smart Chess AI
So what if you got the strongest chess engines, the best databases like the latest Chessbase 26 with access to millions of games from Megabase 26. But these are tools accessible by any chess players as long as they have the money to get them. And they've been around forever.
The evolution of chess is stagnant. Databases and chess engines. Of course we have the chess authors and chess coaches peddling their books and chess courses from Chessables etc. Don't get me wrong, there is potentially tremendous value in those mediums - no questions about that.
It's the new technology that is lacking. For decades, the chess industry has prioritized "brute force" calculation—engines like Stockfish that see 40 moves ahead but lack the vocabulary to explain why a position is winning.
What I feel is missing is computers understanding deeper. We need "smart" AI—one that truly assists human improvement—requires a shift from pure numerical evaluation to tactical pattern recognition.
That just means AI that recognises what is a fork, pin, skewer, discovered attack, double check, back rank weakness, colour weakness and double attack. Wait what? Don't computers already do that ? The simple answer is no. They can pin point which move in a game is a blunder or was brilliant but aren't that great at just recognising if the winning theme was a double check.
Beyond the Centipawn: The Language of Logic
Traditional engines see a board as a mathematical value, often expressed in "centipawns" (e.g., $+1.5$). While precise, this is a "black box" for the average player. A pattern-aware engine, such as the architecture behind ChessGrammar, transforms this math into a geometric map. Yes, there is only ONE project that does this as far as I know, called ChessGrammar (you can Google it to find out more).
By identifying the "grammar" of the game—forks, pins, skewers, and back-rank vulnerabilities—an AI can categorize positions instead of just scoring them.
This classification is the "missing link" in chess tech. When an engine identifies a discovered attack, it isn't just finding a winning move; it is identifying a specific logical theme. For a developer, this means the AI can provide structured data (like JSON) that tells a front-end application exactly what is happening. For the player, it means the feedback is no longer "play Be7," but rather "Be7 works because the Rook on d1 is pinning your Queen."
Patterns are the Secret Sauce!
Personalized Training Data: Imagine now you can pull out your database of games and ask the computer to list the games where there was a winning fork, but you missed it.
Machines that can "See"!
The smartest Chess AI for me isn't the one that wins every game—it’s the one that understands the shapes of the struggle, lets me learn from it.
For the moment, ChessGrammar is closed source, although the programmer allows others to use it via API calls. That is not ideal as it seems that it is the effort of just one person, and his source code is personal, and I assume it is not shareable.
Ideally, someone should make a similar project but open-source it so that it can be improved on by multiple software engineers around the world.
