Kunstig Intelligens til Brætspillet Taiji - Danmarks Tekniske Universitet

124 Bilag A
825 beta = r e [ 2 ] ;
826 i f ( r e [ 0 ] == 1) {
827 b [ c ] [ r −1] = 2 ;
828 b [ c ] [ r −2] = 2 ;
829 r e t u r n ( r e [ 3 ] ) ;
830 }
831 b [ c ] [ r −1] = 2 ;
832 b [ c ] [ r −2] = 2 ;
833 }
834 }
835 }
836 }
837 }
838 }
839 }
840 r e t u r n ( n . a ) ;
841 }
842 }
843
844
845 // haandtere p l a c e r i n g e n a f b r i k k e r f o r max ( ) , c h e c k e r om de
a l l e r e d e e k s i s t e r e r , haandtere alpha beta pruning
846 p r i v a t e i n t [ ] placePieceMax ( i n t [ ] [ ] b , i n t d , Node n , i n t wC,
i n t wR, i n t bC , i n t bR, i n t alpha , i n t beta ) {
847 i n t [ ] p = new i n t [ 3 ] ;
848 p = c h e c k B o a r d I n d i v i d u a l i t y ( b , d ) ;
849 i n t [ ] r e = new i n t [ 4 ] ; // r e [0]= 1 break , r e [1]= alpha , r e
[2]= beta , r e [3]= n . a
850
851 i f ( p [ 2 ] >= 0) {
852 nodes [ p [ 0 ] ] [ p [ 1 ] ] . get ( p [ 2 ] ) . par . add ( n ) ;
853 n . c h i l d r e n . add ( nodes [ p [ 0 ] ] [ p [ 1 ] ] . get ( p
[ 2 ] ) ) ;
854 i n t v = min ( nodes [ p [ 0 ] ] [ p [ 1 ] ] . get ( p [ 2 ] )
, alpha , beta , 1 ) ;
855 i f ( n . a < v )
856 n . a = v ;
857 i f ( n . a > beta ) {
858 r e [ 0 ] = 0 ; // 1 = alpha beta pruning
s l a a e t t i l , 0 = s l a a e t f r a (
s l a a e t f r a da den g i v e r
problemer )
859 r e [1]= alpha ;
860 r e [2]= beta ;
861 r e [3]= n . a ;
862 r e t u r n ( r e ) ;
863 }
864 i f ( alpha < n . a )
865 alpha = n . a ;
866
867 }
868 i f ( p [ 2 ] == −1){
869 p [2]= nodes [ p [ 0 ] ] [ p [ 1 ] ] . s i z e ( ) ;
870 nodes [ p [ 0 ] ] [ p [ 1 ] ] . add ( n . addChild ( tNode .
createChildNode (wC, wR, bC , bR, n ) )