Introduction
Building a chess engine is a rite of passage for many programmers, and I was no exception. I had always been fascinated by chess and wanted to understand the intricacies of how engines like Stockfish and Lc0 worked. So, I decided to take on the challenge of building my own chess engine from scratch in Java.
A visual representation of a chess board
In this article, I'll share my journey, the challenges I faced, and the lessons I learned along the way. I'll also provide some insights into the development process and how I approached various aspects of chess programming.
Why Build a Chess Engine?
Building a chess engine is not just about creating a program that can play chess. It's an opportunity to learn about algorithms, data structures, and optimization techniques. It also provides a deep understanding of the game itself, including its rules, strategies, and complexities.
For me, the motivation was twofold: I wanted to challenge myself as a programmer and gain a deeper appreciation for the game of chess. I had played chess casually for years, but I wanted to understand the mechanics behind the engines that could play at a grandmaster level.
Understanding Bitboards
One of the most important concepts in chess programming is the bitboard. A bitboard is a 64-bit integer where each bit represents a square on the chess board. This representation allows for extremely efficient move generation and position evaluation using bitwise operations.
Bitboard Visualization
Below is a visualization of how bitboards represent piece positions. Click on the buttons to see different piece positions.
Knight moves from e4: All squares a knight can reach in one move.
// Bitboard representation for a Knight at position e4 (square 28) long knightBitboard = 0L; int position = 28; // e4 in 0-63 notation knightBitboard |= (1L << position); // Calculate all possible knight moves from this position long knightMoves = 0L; long knight = knightBitboard; // Knight move patterns: 8 possible moves knightMoves |= ((knight << 17) & ~FILE_A); // up 2, left 1 knightMoves |= ((knight << 10) & ~FILE_AB); // up 1, left 2 knightMoves |= ((knight >> 6) & ~FILE_AB); // down 1, left 2 knightMoves |= ((knight >> 15) & ~FILE_A); // down 2, left 1 knightMoves |= ((knight << 15) & ~FILE_H); // up 2, right 1 knightMoves |= ((knight << 6) & ~FILE_GH); // up 1, right 2 knightMoves |= ((knight >> 10) & ~FILE_GH); // down 1, right 2 knightMoves |= ((knight >> 17) & ~FILE_H); // down 2, right 1
Version 1: A rough start
My first attempt at building a chess engine was far from perfect, but it was an invaluable learning experience. When I first started, I had no idea how complex chess programming could be. I had played chess before and understood the rules well, but coding an engine that could play even at a basic level was a completely different challenge.
At first, I thought the best approach was to represent the board using a simple 64-integer array, where each index represented a square, and each value represented a piece. This seemed straightforward, but as I started implementing move generation, things quickly got messy.
Problems with the Array Approach
Using a 64-integer array for board representation led to several issues:
- Inefficient move generation requiring constant array iterations
- Difficulty implementing complex rules like en passant and castling
- Poor performance when evaluating positions at depth
- Complicated logic for piece movement patterns
I didn't follow a structured approach; instead, I learned as I went, referring to various resources on chess programming. Some of the most useful references I found included:
- The Chess Programming Wiki, which has detailed explanations on everything from move generation to evaluation functions.
- Open-source chess engines like Stockfish and Ethereal, which provided inspiration on efficient coding practices.
- The Stockfish Discord community, where experienced developers discussed best practices and debugging techniques.
Version 1 Limitations
My first version had several critical shortcomings:
- Lacked essential chess rules like en passant and castling
- Used a simple 64-integer array instead of bitboards
- No Universal Chess Interface (UCI) support
- Poor performance due to inefficient data structures
Version 2: A complete overhaul
Determined to build something more efficient, I spent an entire week rewriting my chess engine from the ground up. This time, I implemented bitboards, which significantly improved performance. I also developed a Perft tool to compare my move generation results with Stockfish, ensuring accuracy.
I started by creating a prototype for my Bitboard class. Bitboards were completely new to me, so I spent a good part of the day reading up on bitwise operations and testing small code snippets. My main goal was to understand how shifting bits could help with move generation.
I wrote functions to set, clear, and manipulate bits on a 64-bit integer to represent piece positions efficiently. By the end of the day, I had a basic implementation that I could experiment with, and I was starting to see the advantages of using bitboards over my previous 64-integer array approach.
public class BitboardOperations { // Constants for ranks and files public static final long RANK_1 = 0xFFL; public static final long FILE_A = 0x0101010101010101L; // Get bit at specific position public static boolean getBit(long bitboard, int position) { return ((bitboard >> position) & 1L) != 0; } // Set bit at specific position public static long setBit(long bitboard, int position) { return bitboard | (1L << position); } // Clear bit at specific position public static long clearBit(long bitboard, int position) { return bitboard & ~(1L << position); } }
With a working bitboard structure, I started implementing functions to generate pseudo-legal moves. This was where the real challenge began. Pawns were particularly tricky due to their unique movement and capturing rules, especially en passant. Knights were the easiest to implement, given their fixed jump pattern.
Midway through the day, I realized that storing precomputed attack tables for sliding pieces (bishops, rooks, queens) would make my move generation significantly faster. I started implementing attack masks that would allow me to quickly determine the squares a piece could attack based on occupancy.
This day was all about refining and optimizing my move generator. I considered implementing magic bitboards to speed up move lookups for sliding pieces but decided to focus on other optimizations first. I wrote helper functions to extract moves from bitboards efficiently.
A major milestone was adding move visualization. I created a simple console-based board representation that allowed me to print moves and verify their correctness. Seeing my engine generate and print legal moves was the first moment where I thought, this might actually work.
With move generation mostly implemented, I turned to verification using Perft. I ran my engine at depth 1 and compared results with Stockfish. Everything looked good. Then I tried depth 2. Looked good. And as I tried more and more depths, I was more and more staring at the screen, praying that it would be good. That's when I discovered my engine was brilliantly, perfectly, super bugged. Problems started appearing after depth 4 I think.
| Depth | Flowgine Nodes | Stockfish Nodes | Status |
|---|---|---|---|
| 1 | 20 | 20 | ✓ Match |
| 2 | 400 | 400 | ✓ Match |
| 3 | 8,902 | 8,902 | ✓ Match |
| 4 | 197,742 | 197,281 | ✗ Mismatch |
| 5 | 4,897,256 | 4,865,609 | ✗ Mismatch |
This was one of the most frustrating parts of the project. One tiny bug in move generation could cause thousands of incorrect positions in deeper Perft tests. I spent hours staring at logs, printing board states, and manually checking moves to identify where things were going wrong.
If day 4 was frustrating, day 5 was soul-crushing. I spent the entire day fixing Perft errors, running test cases, and verifying that my engine was generating correct move counts. At one point, I considered giving up. But after hours of debugging, my Perft results finally matched Stockfish at all tested depths. I had done it—my move generator was finally working correctly!
Acknowledgments
Throughout this journey, the Stockfish Discord community was incredibly helpful. Their insights and discussions helped me understand common pitfalls and best practices. I would recommend anyone interested in chess programming to join their community.
Final Thoughts
Building my first chess engine was one of the most rewarding programming experiences I've ever had. While my engine is far from the level of Stockfish or Lc0, it's a project I'm proud of. I continue to revisit it, improving the AI and learning more about optimization. If you're considering building your own chess engine, be prepared for an intense but highly educational challenge!