This project is set up for school course named Advanced Tools

In this project I will compare simple boardgame AI's, for example MiniMax vs Monte Carlo with connect 4. I will be implementing the AI's myself, in a provided engine and provided boardgame setup and rules. Code wise there is an abstract class GameBoard, which has basic functions like CheckWinner(): int, MakeMove(int): int, GetMoves(): List and GetActivePlayer(): int. These functions are implemented on different gameboards, for example ConnectFourBoard. Using this information it is possible to write non-game specific AI's. For better results however game specific AI's may be implemented in the future.

The provided engine is a custom C# engine without interface, complete code-based. It relies on a copyrighted sound library so that will not be uploaded.

In the C# project there is an option to compare two AI's, which can be set to 'Normal Games' or 'Compare AI'. This will automatically enable a tracker which will write the gamedata away to a file (not recommended, but no option to turn it off yet, except by code) and will print this information into the console.

Human // The human player, not really an agent but can play quite well.

Random // Gets all possible moves and picks randomly.

Random+ // Gets all possible moves and picks randomly. However if there is a move which wins directly, it // will pick that move.

Monte Carlo // Plays an amount of games on every possible move on the current board. Then it will play the move // with the highest score (determined on wins).

MiniMax // Recursively loops through all possible board states from the current state and will pick the move // with the best future. This is a heavy AI on bigger games than Tic-Tac-Toe, that's why there is depth // implemented which will keep the AI from searching deeper than said depth.

MiniCarlo // Combination of Monte Carlo and Minimax (Custom name). Will use minimax until a certain depth is //reached, than it will use MonteCarlo, this will save perfermonce on bigger games like connect 4.

Alpha // Alpha Beta pruning. gets the best move the same way as minimax. However, if a node is unnecessary to //'calculate' it will skip that node, making it faster

The CheckersGame folder has a Processing project with a checkers game. Now it is important to notice that there can actually be a draw, though the game does not recognize this yet. In the checkersGame various different AI's will be implemented as well.

Human // The human player, not really an agent but can play quite well.

Random // Gets all possible moves and picks randomly.

Greedy // Picks a random move, but if there is a jump available, it will pick a random jump

Monte Carlo // Plays an amount of games on every possible move on the current board. Then it will play the move // with the highest score (determined on wins).

MiniMax // Recursively loops through all possible board states from the current state and will pick the move // with the best future. This is a heavy AI on bigger games than Tic-Tac-Toe, that's why there is depth // implemented which will keep the AI from searching deeper than said depth.

Alpha Beta Pruning // Alpha Beta pruning. gets the best move the same way as minimax. However, if a node is unnecessary to // 'calculate' it will skip that node, making it faster. This agent also uses quiescence search

Alpha // Combination of Alpha Beta pruning, quiescence search and Monte Carlo for a relatively fast and good result