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GrAph Based bOard game solveR (GABOR) is a solving algorithm/program for a class of board games. That class is board games, where 2 players take alternating turns occupying squares. A player wins, if he manages to occupy all squares of a specific pattern. Real-world examples for this type of game are tic tac toe, Qango or Go-Moku. GABOR can, without modifications, deal with any game of this class, no matter the shape of the board or the layout of the winpatterns.
On http://python.yannikkeller.de/solver/, you can find a community platform for sharing and exploring games and solutions created by GABOR. If you just want to see some of GABORs findings without installing anything, you should probably head over there. However, to start solving your own games, you will have to install GABOR on your computer. You can then head back to http://python.yannikkeller.de/solver/ and share your computed solutions with the world.
Installation instructions are for linux systems. You should also be able to get it to work on Windows or MacOS using the installation instructions for graph-tool.
python3.8+, graph-tool, flask, flask-socketio, psutil
GABOR depends on graph-tool, which is easiest installed via conda. If you have conda ready, you can install from environment.yml via
conda env create -f environment.yml. Activate the env via
conda activate GABOR
If the install from
environment.yml fails, you can try installing manually with the commands
conda install -c conda-forge graph-tool
conda activate gt
pip install flask flask_socketio psutil
export FLASK_APP=flask_server.py;flask run from the projects root. Head over to localhost:5000 in your favorite browser. To start solving a game, click the on a
Analyze link and then
Solve game for black/white. GABOR performs binary game evaluation, so when solving for black, you are checking if the game is won for black or not (White wins or draw). To fully solve a game, you will need to try and solve for black as well as for white.
GABOR transforms any board game into a graph. Each winpattern will be a vertex and each square will be a vertex. Squares that are part of a winpattern share an edge with that winpattern. When a player occupies a square, that square will be removed from the graph and any connected winpattern vertex will be colored in that players color. If the winpattern vertex was already in the players color and has no edges left, the player wins. If the winpattern was already colored in the opponents player color, the winpattern vertex is removed. Any square vertex left without edges as a result is removed.
That way of representing the board game has the following advantages:
Inspired by the paper Go-Moku and Threat-Space Search, GABOR uses a combination of Proof-Number search and Threat-Space Search to solve games. A big advantage of GABORs graph representation are the transpositions/symetries/graph-isomorphisms. Thus, GABORs search structure is not a tree, as in the original Proof-Number search, but instead a directed acyclic graph (DAG). I used the practical algorithm described here to make PN-search work on my DAG. Also, GABOR's implementation of the threat-space is not specifically built for a single game, as in the original papers implementation, but exploits the graph structure to know what a threat is and what not.
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