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I have problem with my own Chess Engine using minimax algorithm to search for chess moves I use a 5 plies depth search and with only material/bonus/mobility evaluation , but it also make dumb moves and sacrifices valuable pieces even when I give to them infinity (which is sure a search problem), I'm not using any types of pruning and gives a 5 depth search result in few seconds.

I'm stuck in this problem for a week, I am sure the problem is with the Backtracking, not the Chess Logic (so anyone with no chess background would solve this :)) I hope you guys won't Disappoint me :)

Here is the simple search code:

int GameControl::Evaluate(ChessBoard _B) { int material=0,bonus=0,mobility=0; for(int i=0;i<8;i++) for(int j=0;j<8;j++) { if(_B.Board[i][j]!=EMPTY) { if(_B.Board[i][j]->pieceColor==WHITE){ material+=-_B.Board[i][j]->Weight; bonus+=-_B.Board[i][j]->bonusPosition[i][j]; mobility+=-_B.Board[i][j]->getPossibleMovesList(i,j,B).size(); } else { material+=_B.Board[i][j]->Weight; bonus+=_B.Board[i][j]->bonusPosition[i][j]; mobility+=_B.Board[i][j]->getPossibleMovesList(i,j,B).size(); } } } return material+bonus/10+mobility/20; } pair<pair<int,int>,pair<int,int>> GameControl::minimax( int depth , ChessBoard _B ) { short int i,j; int bestValue = -INFINITY; pair<pair<int,int>,pair<int,int>> bestMove; vector< pair<int,int> > ::iterator it; vector< pair<int,int> > Z; for( i = 0; i < 8; i++ ) for( j = 0; j < 8; j++ ) { if(_B.Board[i][j]!=EMPTY && _B.Board[i][j]->pieceColor==BLACK ) { Z=_B.Board[i][j]->getPossibleMovesList(i,j,_B); for(it=Z.begin();it!=Z.end();it++) { pair<int,int> temp; temp.first=i,temp.second=j; ChessPieces* DestinationPiece; DestinationPiece=_B.Board[(*it).first][(*it).second]; //Moving _B.Board[(*it).first][(*it).second]=_B.Board[i][j]; _B.Board[i][j]=EMPTY; // int value = minSearch( depth-1 , _B ); if( value > bestValue ) { bestValue = value; bestMove.first.first = i; bestMove.first.second = j; bestMove.second.first = (*it).first; bestMove.second.second = (*it).second; } //Undo Move _B.Board[i][j]=_B.Board[((*it).first)][(*it).second]; _B.Board[(*it).first][(*it).second]=DestinationPiece; } } } return bestMove; } int GameControl::minSearch( int depth , ChessBoard _B ) { short int i; short int j; if(depth==0) return Evaluate(_B); int bestValue = INFINITY; for( i = 0; i < 8; i++ ) for( j = 0; j < 8; j++ ) { vector< pair<int,int> > ::iterator it; vector< pair<int,int> > Z; if(_B.Board[i][j]!=EMPTY && _B.Board[i][j]->pieceColor==WHITE && !_B.Board[i][j]->V.empty()) { Z=_B.Board[i][j]->getPossibleMovesList(i,j,_B); for(it=Z.begin();it!=Z.end();it++) { pair<int,int> temp; temp.first=i; temp.second=j; ChessPieces* DestinationPiece; DestinationPiece=_B.Board[(*it).first][(*it).second]; // Moving _B.Board[(*it).first][(*it).second]=_B.Board[i][j]; _B.Board[i][j]=EMPTY; // int value = maxSearch( depth-1 , _B ); if( value < bestValue ) bestValue = value; //Undo Move _B.Board[i][j]=_B.Board[(*it).first][(*it).second]; _B.Board[(*it).first][(*it).second]=DestinationPiece; // } } } return bestValue; } int GameControl::maxSearch( int depth , ChessBoard _B ) { short int i; short int j; if(depth==0) return Evaluate(_B); vector< pair<int,int> > ::iterator it; vector< pair<int,int> > Z; int bestValue = -INFINITY; for( i = 0; i < 8; i++ ) for( j = 0; j < 8; j++ ) { if(_B.Board[i][j]!=EMPTY && _B.Board[i][j]->pieceColor==BLACK ) { Z=_B.Board[i][j]->getPossibleMovesList(i,j,_B); for(it=Z.begin();it!=Z.end();it++) { pair<int,int> temp; temp.first=i,temp.second=j; ChessPieces* DestinationPiece; DestinationPiece=_B.Board[(*it).first][(*it).second]; //Moving _B.Board[(*it).first][(*it).second]=_B.Board[i][j]; _B.Board[i][j]=EMPTY; // int value = minSearch( depth-1 , _B ); if( value > bestValue ) bestValue = value; //Undo Move _B.Board[i][j]=_B.Board[((*it).first)][(*it).second]; _B.Board[(*it).first][(*it).second]=DestinationPiece; } } } return bestValue; }

by (108k points)

You have to perform the quiescence search as it decreases the effect of the horizon problem faced by AIengines for various games like chess. At a minimum, you should extend the search for any forced moves, checks or captures where a piece captures one of equal or greater value.

Minimax is a decision rule used in Artificial Intelligence so if you wish to learn more about Minimax Decision Rule then visit this Artificial Intelligence Course.