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.
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.
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. Checking for legal moves required iterating through the entire board constantly, and handling special rules like en passant and castling was far more complex than I had anticipated.
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.
Despite my initial enthusiasm, the first version of my engine had severe limitations. It lacked essential chess rules like en passant and castling. Instead of using bitboards, I relied on a simple 64-integer array to represent the board, which quickly became a nightmare. It was cumbersome to work with, and I immediately realized that this was the primary reason for my engine’s poor performance. Additionally, there was no Universal Chess Interface (UCI) support—just basic commands to play moves.
Despite its shortcomings, this initial version taught me crucial lessons that would help me tremendously when I decided to start over from scratch.
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.
Day 1: Experimenting with Bitboards
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.
Day 2: Move Generation Begins
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.
By the end of the day, I had some basic movement rules implemented, but they were not yet optimized.
Day 3: Completing and Optimizing Move Generation
This day was all about refining and optimizing my move generator. I thought about implemented magic bitboards to speed up move lookups for sliding pieces but I didn't. I know that it would give a massive performance boost compared to naive methods but it wasn't my priority. Instead, I optimized as much as I could. I also 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.
Day 4: Perft Debugging – Welcome to Hell
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.
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. Rook moves were occasionally skipping squares. En passant was randomly failing in certain edge cases. I fixed a few issues, reran the tests, and—more bugs appeared.
By the end of the day, I had written a debugging tool that allowed me to compare move lists with Stockfish in real time. This made the debugging process slightly less painful.
Day 5: More Debugging – A Test of Patience
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!
Day 6: Implementing the Search Algorithm
With move generation fixed, I finally got to work on the actual chess engine logic. I implemented a basic minimax search with alpha-beta pruning. This allowed my engine to look ahead multiple moves and evaluate positions. I also wrote a simple evaluation function that considered material balance.
Later in the day, I added a function to export games in PGN format, which allowed me to save and analyze games played by my engine.
Days 7-8: Final Touches and UCI Implementation
I spent these final days cleaning up the code and making the engine compatible with the UCI protocol. This meant my engine could now communicate with chess GUIs like CuteChess and Arena. Testing it in a real GUI felt incredibly rewarding. Finally, I made some final optimizations to improve search speed.
Lessons Learned and Future Improvements
Once I attempted to make my engine stronger, I realized how incredibly difficult chess engine development is. Writing a basic move generator is one thing, but creating a strong search algorithm requires deep knowledge of advanced algorithms and chess heuristics.
The Challenge of Optimization
Optimizing a chess engine is a massive challenge because:
- Every millisecond counts – Even small inefficiencies in move generation can slow down the engine significantly when searching millions of positions per second.
- Pruning is essential – Without techniques like alpha-beta pruning, an engine evaluates far too many useless positions.
- Heuristic evaluation is complex – Evaluating positions effectively requires deep chess knowledge and extensive tuning.
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!