chessmate-master.rar_chess

# Chessmate Chess AI High-school Computer Class Project in Java ## How to run Run `src/build.bat` to compile and run. You will need the Java SDK. You can beat her easily on a depth of 4, but if you give it ~20-30s at depth setting of 5, she plays a decent game. I wrote this Java chess engine eight years ago in 2005 for my grade 12 high school project. I was 17 at the time, so I thought the code would be really bad, but it still works and beats me most of the time, bearing in mind that I'm not a very good chess player. It won a regional prize or something (cash must have gotten lost in the mail). I'm pretty proud of it :).  ## Limitations Chessmate has no opening book and cannot castle, take en passant or promote pawns. If you harbor sado-masochistic tendencies, you can easily modify the `Board` class and `positionEvaluation` heuristic function to add castling. ## How does it work? Chessmate uses an iterative deepening minimax search algorithm with alpha-beta pruning and simple horizon detection during exchanges with an original heuristic function. That's a fancy way of saying it builds a tree of moves, rates each move with a number (using a heuristic function), then keeps exploring the leaf branches that seem promising. Minimax assumes its opponent will make the best sequence of moves it can think of and will then counter with a move that minimises your opportunities while maximising its own. ## Performance Chessmate has a terrible early game due the lack of an opening book, an average medium game and a pretty strong end game. It runs pretty fast for a Java application. I remember it searching through 500k moves/second on my shitty PC in 2005. I now get about 1.2M nodes/second on my Macbook Air running on a single thread. ## Database? The project had a database requirement, but I ripped out the Access database prompt. It's mostly a gimmick that tracks a history of moves. ## Heuristic Function The original heuristic function is quite simple, but performs well given sufficient search depth (5 seems to be the sweet spot). The `positionEvaluation` method returns a `float` value that is positive or negative based on whether it's winning or losing, respectively. It also uses some of the board control data set in the `setControlData` method. The following factors are weighted to evaluate each board position: - Material gain (sum of the value of your pieces minus the opponent's) - Attacking the opponent's pieces - Defending it's own pieces - Controlling the board, with extra weight for controlling the four squares in the centre of the board. The structure of these evaluation functions are terrible and really hard to unit test. Having read through the evaluation code, I am almost certain that the board control data is fundamentally broken. Oh well.