Winter is a UCI chess engine.

It is the top rated chess engine from Switzerland and has competed at top invite only computer chess events such as the Top Chess Engine Competition and the Chess.com Computer Championships. Its level of play is super human, but below the state of the art reached by the large, distributed and resource intensive open source projects Stockfish and Leela Chess Zero.

Winter has relied on many machine learning algorithms and techniques over the course of its development. This includes certain clustering methods not used in any other chess programs, such as Gaussian Mixture Models and Soft K-Means.

As of Winter 0.6.2, the evaluation function relies on a small neural network for more precise evaluations.

In order to run it on Linux, just compile it via "make" in the root directory and then run it from the root directory.

The makefile will assume you are making a native build, but if you are making a build for a different system, it should be reasonably straightforward to modify yourself.

Winter does not rely on any external libraries aside from the Standard Template Library. All algorithms have been implemented from scratch. As of Winter 0.6.2 I have started to build an external codebase for neural network training.

Compiling for android is only a bit more complicated than compiling for Linux. Follow the following instructions.

1. Download and extract Android's NDK.
2. Export your NDK path. For example $ export PATH=$PATH:$HOME/android-ndk-r21d/toolchains/llvm/prebuilt/linux-x86_64/bin
3. Switch to the Winter root directory $ cd Winter
4. Get the sse2neon header file. $ wget -O src/learning/sse2neon.h https://raw.githubusercontent.com/DLTcollab/sse2neon/master/sse2neon.h
5. Build!$ make ARCH=aarch64(64-bit) or $ make ARCH=armv7a(32-bit) $ aarch64-linux-android-strip Winter

Winter versions 0.7 and later have support for contempt settings. In most engines contempt is used to reduce the number of draws and thus increase performance against weaker engines, often at the cost of performance in self play or against stronger opposition.

Winter uses a novel contempt implementation that utilizes the fact that Winter calculates win, draw and loss probabilities. Increasing contempt in Winter reduces how much it values draws for itself and increases how much it believes the opponent values draws.

As of version v0.8.1 disabled. If I don't remember to add this again before v0.9, please pester me with an issue on github!

Internally Winter actually tends to have a negative score for positive contempt and vice versa. This is natural as positive contempt is reducing the value of a draw for the side to move, so the score will be negatively biased.

In order to have more realistic score outputs, Winter does a bias adjustment for non-mate scores. The formula assumes a maximum probability for a draw and readjusts the score based on that. This results in an overcorrection. Ie: positive contempt values will result in a reported upper bound score and negative contempt values will result in a reported lower bound score.

For high contempt values it is recommended to adjust adjudication settings.

An increasingly popular format in human chess is Armageddon. Winter is the first engine to the author's knowledge to natively support Armageddon play as a UCI option. Internally this works by setting contempt to high positive value when playing white and a high negative value when playing black.

At the moment contempt is not set to the maximum in Armageddon mode. In the limited testing done this proved to perform more consistently. This may change in the future.

In Armageddon mode score recalibration is not performed. The score recalibration formula for regular contempt assumes the contempt pushes the score away from the true symmetrical evaluation. In Armageddon the true eval is not symmetric.

As of version v0.8.1 disabled. If I don't remember to add this again before v0.9, please pester me with an issue on github!

At the moment training a neural network for use in Winter is only supported in a very limited way. I intend to release the script shortly which was used in order to train the initial 0.6.2 net.

In the following I describe the steps to get from a pgn game database to a network for Winter.

1. Get and compile the latest pgn-extract by David J. Barnes.
2. Use pgn-extract on your .pgn file with the arguments -Wuci and --notags. This will create a file readable by Winter.
3. Run Winter from command line. Call gen_eval_csv filename out_filename where filename is the name of the file generated in 2. and out_filename is what Winter should call the generated file. This will create a .csv dataset file (described below) based on pseudo-quiescent positions from the input games.
4. Train a neural network on the dataset. It is recommended to try to train something simple for now. Keep in mind I would like to refrain from making Winter rely on any external libraries.
5. Integrate the network into Winter. In the future I will probably support loading external weight files, but for now you need to replace the appropriate entries in src/net_weights.h.
6. make clean and make

The structure of the .csv dataset generated in 3. is as follows. The first column is a boolean value indicating wether the player to move won. The second column is a boolean value indicating whether the player to move scored at least a draw. The remaining collumns are features which are somewhat sparse. An overview of these features can be found in src/net_evaluation.h.