The Modules API

The sample players contain examples showing how to use the modules API

.minmax(board, depth, score)

Find the best move by using the minmax algorithm with alpha-beta pruning.

.minmax accepts three arguments:

• board is a FEN string representing the current position.
• depth is an positive integer specifying how deep the algorithm should traverse the decision tree before applying the score function.
• score is a function accepting a chess.js object as an input argument, and returning a number between Number.NEGATIVE_INFINITY and Number.POSITIVE_INFINITY quantifying how good that position is (positive is better) for the player currently moving.

// JSFiddle
Modules.minmax(board, depth, score);

// node
var minmax = require('./modules/minmax.js');
minmax.move(board, depth, score);

.deepening(board, depth, score, span)

Find the best move by using a minmax inspired iterative deepening algorithm.

.deepening accepts four arguments:

• board same as for .minmax.
• depth same as for .minmax.
• score same as for .minmax.
• span is a value determining the number of most promising moves that should be explored when the algorithm descends the decision tree.

// JSFiddle
Modules.deepening(board, depth, score, span);

// node
var deepening = require('./modules/decender.js');
deepening.move(board, depth, score, span);

.endgame(board)

Find the best move by performing a lookup in a endgame tablebase containing the best moves for all positions with five or fewer pieces. The actual tablebase is located on cheslie-endgame.azurewebsites.net. If the lookup fails, a random legal move is returned.

.endgame accepts one argument:

• board is a FEN string representing the current position.

// JSFiddle
Modules.endgame(board);

// node
var endgame = require('./modules/endgame.js');
endgame.move(board);