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// Step 1: Move generation and board visualization // We’ll use the chess.js library for move generation, and chessboard.js for visualizing the board. // The move generation library basically implements all the rules of chess. // Based on this, we can calculate all legal moves for a given board state. // A visualization of the move generation function. The starting position is used as input and the output is all the possible moves from that position. // Using these libraries will help us focus only on the most interesting task: creating the algorithm that finds the best move. // We’ll start by creating a function that just returns a random move from all of the possible moves: function makeRandomMove() { var possibleMoves = game.moves(); // game over if (possibleMoves.length === 0) return; var randomIndex = Math.floor(Math.random() * possibleMoves.length); game.move(possibleMoves[randomIndex]); board.position(game.fen()); } // Step 2: Position evaluation // Now let’s try to understand which side is stronger in a certain position. // The simplest way to achieve this is to count the relative strength of the pieces on the board using the following table: // With the evaluation function, we’re able to create an algorithm that chooses the move that gives the highest evaluation: // The only tangible improvement is that our algorithm will now capture a piece if it can. function evaluateBoard() { var totalEvaluation = 0; for (var i = 0; i < 8; i++) { for (var j = 0; j < 8; j++) { totalEvaluation = totalEvaluation + getPieceValue(board[i][j], i, j); } } return totalEvaluation; } // Step 3: Search tree using Minimax // Next we’re going to create a search tree from which the algorithm can chose the best move. // This is done by using the Minimax algorithm. // In this algorithm, the recursive tree of all possible moves is explored to a given depth, and the position is evaluated at the ending “leaves” of the tree. // After that, we return either the smallest or the largest value of the child to the parent node, depending on whether it’s a white or black to move. (That is, we try to either minimize or maximize the outcome at each level.) function minimax(depth, game, isMaximisingPlayer) { if (depth === 0) { return -evaluateBoard(game.board()); } var possibleMoves = game.moves(); if (isMaximisingPlayer) { var bestMove = -9999; for (var i = 0; i < possibleMoves.length; i++) { game.move(possibleMoves[i]); bestMove = Math.max(bestMove, minimax(depth - 1, game, !isMaximisingPlayer)); game.undo(); } return bestMove; } else { var bestMove = 9999; for (var i = 0; i < possibleMoves.length; i++) { game.move(possibleMoves[i]); bestMove = Math.min(bestMove, minimax(depth - 1, game, !isMaximisingPlayer)); game.undo(); } return bestMove; } } // Step 4: Alpha-beta pruning // Alpha-beta pruning is an optimization method to the minimax algorithm that allows us to disregard some branches in the search tree. // This makes the algorithm faster and more efficient. // Here’s the final version of our algorithm: function calculateBestMove() { var possibleMoves = game.moves(); var
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