/**
* Alpha-Beta Negamax algorithm. Go check on wikipedia!
*
* @param evaluatedBoard The board currently evaluated.
* @param resultBoard (Optional) The board that will store the result of the best move.
* @param alpha Alpha parameter, to cut some leaves on the multiverse tree.
* @param beta Alpha parameter, to cut even more leaves on the multiverse tree.
* @param depth The current depth of the reflexion.
* @return The best you can do with the worst the other player can do with the best you can do
* with...
*/
public int negaMax(Board evaluatedBoard, Board resultBoard, int alpha, int beta, int depth) {
// If it's a leaf, evaluate.
if (depth == MAX_DEPTH
|| evaluatedBoard.numberOf(DWARF) == 0
|| evaluatedBoard.numberOf(TROLL) == 0) {
return evaluate(evaluatedBoard);
}
List<Board> childrenBoards = evaluatedBoard.childrenBoards();
/* Map<Board, Integer> boardValues = new HashMap<>();
for (Board childrenBoard : childrenBoards)
{
boardValues.put(childrenBoard, evaluate(childrenBoard));
}
// Sort those boards with the evaluation method, to evaluate the most interesting moves.
Collections.sort(childrenBoards, (board1, board2) -> Integer.compare(boardValues.get(board2), boardValues.get(board1))); */
// Look for the best value on children boards.
int boardIndex;
int bestValue = -INFINITY;
for (boardIndex = 0; boardIndex < childrenBoards.size(); boardIndex++) {
// Update the progress callback, if any.
if (depth == 0 && progressCallback != null) {
progressCallback.accept(100 * boardIndex / childrenBoards.size());
}
Board childBoard = childrenBoards.get(boardIndex);
// Get the score of the child. The negamax, a specific implementation of the Minimax, requires
// switching and inverting values here.
int score = -negaMax(childBoard, null, -beta, -alpha, depth + 1);
if (score > bestValue) {
bestValue = score;
// If we're at depth zero, keep track of the best board.
if (depth == 0) {
if (resultBoard != null) {
resultBoard.set(childBoard);
}
}
}
// Alpha-beta pruning
alpha = Math.max(alpha, score);
if (alpha >= beta) {
break;
}
}
return bestValue;
}
