Some time ago I thought that it would be nice to have a programming project again. It should not be too simple, such as writing a sudoku solver, and on the other hand it should not be too complex. I thought about fluid simulations, but then discarded the thought, as I am probably missing the background knowledge.

So, my thoughts came to a chess engine. As that may qualify for a nice programming project, I thought very hard about the design of a chess engine. This post is what I learned in the process. Note that parts of it may be wrong. Maybe, I will soon embark on this adventure.


A chess engine is a computer program that can play chess. An artificial intelligence, if you want to call it like that. The current state is that Chess can be played by computers better than by humans. In 1996, there was the famous match, where Deep Blue, a computer program, won against the chess world champion Gary Kasparov. Nevertheless, chess is far from being a solved game. For example, it is not known if for optimal play, the first player always wins or if the game ends in a draw.

In order for a program to play chess, there need to be several modules which will be explained in detail in the following. There needs to be a representation of the board, as well as knowledge of the rules. Then, the game tree needs to be computed. This tree contains a board state at each node, and the links between the nodes are the valid state transitions, i.e., the valid moves. Note that this tree is too large to be fully computed. Thus, it can only be partially computed. Additionally, note that the board representation needs to be small in order to create this tree fast. Next, we need an evaluation function which helps us to evaluate the different game states. We need a search strategy in order to search through the partial tree and find the best move. Finally, an interface is needed which is used to communicate with the chess engine.

Board Representation and Game State

A chess board is exactly 8x8 fields large. As a funny coincidence, current CPUs have registers of 64 bit length. Consequently, a bunch of optimizations are possible, as 8*8=64. Each piece can be represented on an own board. This is 64 bits, where all bits except one of them are zero. The remaining bit that is one describes the position of the piece. These boards are called Bitboards.

Using various Bit-fiddling approaches, the valid moves can be computed. Note that this is easy for knights, as their movement cannot be obscured by other pieces. Other pieces such as a rook, on the other hand cannot jump over other pieces. Therefore their implementation is slightly trickier, as pieces in their path need to be taken into account. One technique for that are Magic Bitboards.

Additionally to the board, the pieces and their movements, there are various special moves that need to be taken into account in order to go from a board with positions to the game state. The following come to mind:

  • en passant
  • 50 move rule
  • threefold repetition
  • castling

En Passant

This is a special move that can only be performed by pawns, but only dependent on the movement on another pawn. Wikipedia probably has a better explanation. I think that en passant can be implemented by using an additional en passant bitboard that describes the fields to which an en passant move is possible.

50 Move Rule

If for 50 moves, there was no capture, the game is declared a draw. This can be implemented by an additional clock in the game state that counts the moves and is increased for each move. When a capture happens, it is reset to zero.

Threefold Repetition

If a board state is encountered three times, the game is declared a draw. In order to implement this rule, the game state needs to remember the last positions. This is bad, as that probably takes a lot of memory and thus is slow. The last positions can be pruned, if a piece is captured. There are various better ways to implement this. I have not researched much into this topic, but apparently it is possible with a special hash table. Well, this is probably the special rule that will need the most lines of code for extra treatment.


Well, another special move. This one can be done on the kingside and on the queenside, but only if the king is not in check, has not been moved, and if none of the passing fields are under attack. This one can probably be implemented simply by setting a bit.

The Game Tree

In order to build the game tree, there needs to be a move-generating function. This function needs to be fast, as all the moves correspond to nodes in the tree. There is often even special evaluation code for the speed of the generation of the movements. On the tree, the Minimax Algorithm is used. This algorithm computes the next move with the minimum maximal loss. In our case, we give each move a number that corresponds to the advantage of one side. Assuming optimal play from the opponent (the maximal loss), we want to minimize that by choosing our move. As chess is a zero-sum game (the advantage of one side is the disadvantage of the other side), we can use Negamax which computes the same result as Minimax, but is simpler to implement. Of course, this can be enhanced by using various heuristics, such as alpha-beta pruning or the Killer heuristic. As the full game tree cannot be computed, the evaluation of the advantage is done in a certain depth of the game tree, such as six. In that case, the engine would compute six moves ahead.

Evaluation Function

At first it seems that the whole magic of the chess engine is in the evaluation function. However, that is not completely true. The depth of the evaluation also certainly plays an important role. The most common assignment of values to game pieces is pawn=1, bishop=3, knight=3, rook=5, queen=9. Note that the value additionally depends on the position of the piece. Thus, the pieces can be weighted by being more in the center.

The Horizon Effect

The horizon effect occurs, if the search tree is only evaluated to a fixed depth. Attacks beyond that depth (beyond the horizon) are not evaluated. As an example, assume that the game tree is searched up to depth 10. Then, if we can capture the queen after ten moves, this move is evaluated high. However, it may be the case, that we lose our queen certainly in move eleven. The chess engine will not be able to detect this threat. Additionally, sometimes sacrifices of pieces can lead to better positions. A technique to mitigate this problem is called Quiescence Search. Basically, in the end node, all paths consisting of immediate captures are searched, until the node is ‘quiet’, and then the evaluation is performed.

The Monte Carlo Tree Search is a separate approach to chess engines that does not require an evaluation function. But first, a little bit of history. Ten years ago, it was unthinkable that in the board game Go computers will ever beat humans. It was thought that in order to play Go well, a lot of pattern recognition and other algorithmic hurdles have to be tackled. Go has an even larger game tree than chess, and thus even partial search is futile. Well, then Google developed AlphaGo, a computer program that beat the Go grandmaster Lee Sedol. The algorithmic improvements of AlphaGo were then used to play chess, leading to AlphaZero. AlphaZero then crushed the then strongest chess engine Stockfish. As Stockfish is open-source, the guys from Stockfish thought that the algorithmic improvements from AlphaZero should also be available in an open-source form. This lead to the development of Leela Chess Zero.

This new breed of chess engines does not use an evaluation function, but only knows the rules of chess. It then trains an internal neural network by playing against itself. I have not yet researched the details, as I do not have enough money to train such a beast after programming one, but what I understood is that it makes use of Monte Carlo Tree Search. In this algorithm, the game states are not evaluated by an evaluation function, but rather by randomly playing a node to the end of the game. The evaluation of that node is then the expected value of the win. Note that this gives only the ‘average’ score of nodes. In cases when there are only few good moves, and most of the moves are bad, Monte Carlo tree search may not find the good moves and may falsely infer that the move is bad.


In order for the engine to play, there needs to be an interface to the real world. Luckily, there is the UCI protocol. This protocol communicates over clear text and allows to play chess. As an example, it is possible to hook up the engine via telnet to a remote chess server such as FICS and let it play there.

Additionally there are multiple notations for chess moves and board positions. These can be helpful in debugging.

For board positions, there is Forsyth-Edwards Notation and Extended Position Description. As far as I know, this is only useful, when chess is started in other positions as the default one. FEN and EPD are not really human-readable, and for debugging, pretty printing the board with the usual signs (r=rook, p=pawn, …) seems easier to me.

There is Portable Game Notation. This can record chess games and seems to look very useful to be. PGN can be converted to board positions by playing through the game. After the metadata, the heart of PGN consists of the description of the chess moves in algebraic notation.

Opening and Ending

The opening and ending are often handled separately.


In the opening, we are at the start of the tree and cannot really decide between good and bad moves, as the tree has such an immense depth. However, there are standard openings in human games. We may think that humans have evolved chess through centuries of evolution and thus these openings are good. In that case, we can give our chess engine a book of openings which it can use. This is just a really long table on what to do in the first five moves.


In the ending, it may be the case that only three pieces are on the board and that there is a wide variety of moves. However, computing through all these moves may take a long time. For this case, there are special endgame tables. These can be used to see if the current game is won, lost, or a draw.


Most of the described parts are optional optimizations. In general, only a board representation, a movement generating function, as well as the search tree with its evaluation is needed. Additionally, it may be of interest, that chess engines are not as complicated as they once have been. DeepBlue was designed by Masterminds using state of the art technology. Currently, a chess engine is in the reach of a hobby programming project.


12 January 2020