Kunstig Intelligens til Brætspillet Taiji - Danmarks Tekniske Universitet

More documents

Recommendations

Info

212 Bilag A 212 System . out . p r i n t l n ( n . c h i l d r e n . get ( i ) . nodeBoard [ 0 ] [ 2 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 1 ] [ 2 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 2 ] [ 2 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 3 ] [ 2 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 4 ] [ 2 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 5 ] [ 2 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 6 ] [ 2 ] ) ; 213 i f ( tModel . noRows > 3) { 214 System . out . p r i n t l n ( n . c h i l d r e n . get ( i ) . nodeBoard [ 0 ] [ 3 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 1 ] [ 3 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 2 ] [ 3 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 3 ] [ 3 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 4 ] [ 3 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 5 ] [ 3 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 6 ] [ 3 ] ) ; 215 i f ( tModel . noRows > 4) { 216 System . out . p r i n t l n ( n . c h i l d r e n . get ( i ) . nodeBoard [ 0 ] [ 4 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 1 ] [ 4 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 2 ] [ 4 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 3 ] [ 4 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 4 ] [ 4 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 5 ] [ 4 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 6 ] [ 4 ] ) ; 217 i f ( tModel . noRows > 5) { 218 System . out . p r i n t l n ( n . c h i l d r e n . get ( i ) . nodeBoard [ 0 ] [ 5 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 1 ] [ 5 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 2 ] [ 5 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 3 ] [ 5 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 4 ] [ 5 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 5 ] [ 5 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 6 ] [ 5 ] ) ; 219 i f ( tModel . noRows > 6) { 220 System . out . p r i n t l n ( n . c h i l d r e n . get ( i ) . nodeBoard [ 0 ] [ 6 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 1 ] [ 6 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 2 ] [ 6 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 3 ] [ 6 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 4 ] [ 6 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 5 ] [ 6 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 6 ] [ 6 ] ) ; 221 i f ( tModel . noRows > 7) { 222 System . out . p r i n t l n ( n . c h i l d r e n . get ( i ) . nodeBoard [ 0 ] [ 7 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 1 ] [ 7 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 2 ] [ 7 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 3 ] [ 7 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 4 ] [ 7 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 5 ] [ 7 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 6 ] [ 7 ] ) ; 223 i f ( tModel . noRows > 8) {
A.15 <strong>Taiji</strong>Print.java 213 224 System . out . p r i n t l n ( n . c h i l d r e n . get ( i ) . nodeBoard [ 0 ] [ 8 ] + ” ”+ n . c h i l d r e n . get ( i ) . nodeBoard [ 1 ] [ 8 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 2 ] [ 8 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 3 ] [ 8 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 4 ] [ 8 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 5 ] [ 8 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 6 ] [ 8 ] ) ; 225 226 } 227 } 228 } 229 } 230 } 231 } 232 System . out . p r i n t l n ( ) ; 233 } 234 } 235 236 // f o r b r a e t med 8 k o l o n e r 237 p u b l i c void p r i n t C h i l d r e n 8 x ( Node n ) { 238 System . out . p r i n t l n (” t P r i n t P r i n t Children ”+n ) ; 239 f o r ( i n t i =0; i 3) {
Page 1 and 2:
Kunstig Intelligens til Brætspille
Page 3:
Summary This project concerns the d
Page 7:
Forord Dette speciale er udarbejdet
Page 10 and 11:
viii INDHOLD 5.3 Hash funktioner .
Page 12 and 13:
2 Taiji Figur 1.1: Standard Taiji b
Page 14 and 15:
4 Taiji 1.1.1 Forskelle mellem de o
Page 16 and 17:
6 Taiji Alts˚a kan der maksimalt f
Page 18 and 19:
8 Taiji Figur 1.9: Et standard Taij
Page 20 and 21:
10 Taiji Der er dog en række speci
Page 22 and 23:
12 Taiji Figur 1.15: Eksempel p˚a
Page 24 and 25:
14 Taiji 1.4 Spillets kompleksitet
Page 26 and 27:
16 Taiji Figur 1.18: Alle trækmuli
Page 28 and 29:
18 Taiji Figur 1.21: Denne figur vi
Page 30 and 31:
20 Taiji
Page 32 and 33:
22 Design og brugervenlighed Figur
Page 34 and 35:
24 Design og brugervenlighed naturl
Page 36 and 37:
26 Design og brugervenlighed muligh
Page 38 and 39:
28 Design og brugervenlighed 2.2.2
Page 40 and 41:
30 Design og brugervenlighed uprakt
Page 42 and 43:
32 Kunstig Intelligens 3.1.1 TaijiD
Page 44 and 45:
34 Kunstig Intelligens 3.1.10 Figur
Page 46 and 47:
36 Kunstig Intelligens 3.2.1 Nodes:
Page 48 and 49:
38 Kunstig Intelligens at udvide de
Page 50 and 51:
40 Minimax først søgningen, da de
Page 52 and 53:
42 Minimax 4.3 Spilgraf for 3x3 Tai
Page 54 and 55:
44 Minimax Herunder ses spilgrafen
Page 56 and 57:
46 Minimax Herunder ses spilgrafen
Page 58 and 59:
48 Minimax Som det kan ses ender sp
Page 60 and 61:
50 Minimax Det kan ses at alle de e
Page 62 and 63:
52 Optimering af Minimax Figur 5.1:
Page 64 and 65:
Page 66 and 67:
56 Optimering af Minimax som et uni
Page 68 and 69:
58 Optimering af Minimax Det er der
Page 70 and 71:
60 Optimering af Minimax klarer sig
Page 72 and 73:
62 Optimering af Minimax IF v < m T
Page 74 and 75:
64 Optimering af Minimax er tilfæl
Page 76 and 77:
Page 78 and 79:
68 Optimering af Minimax vigtigt at
Page 80 and 81:
70 Optimering af Minimax
Page 82 and 83:
72 Begrænsning af antallet af unde
Page 84 and 85:
Page 86 and 87:
Page 88 and 89:
78 Test og sammenligning af de impl
Page 90 and 91:
Page 92 and 93:
Page 94 and 95:
Page 96 and 97:
Page 98 and 99:
Page 100 and 101:
Page 102 and 103:
92 Konklusion selv n˚a igennem et
Page 104 and 105:
94 Konklusion
Page 106 and 107:
96 Bilag A 19 20 // i n i t i a l i
Page 108 and 109:
98 Bilag A 120 nodes [ p [ 0 ] ] [
Page 110 and 111:
100 Bilag A 218 i f ( n . a > beta
Page 112 and 113:
102 Bilag A 319 p [2]= nodes [ p [
Page 114 and 115:
104 Bilag A 419 420 421 422 423 424
Page 116 and 117:
106 Bilag A 517 n . wr = tModel . n
Page 118 and 119:
108 Bilag A 61 } 62 } 63 64 // c h
Page 120 and 121:
110 Bilag A 155 b [ c +1][ r −1]
Page 122 and 123:
112 Bilag A 251 b [ c −1][ r −1
Page 124 and 125:
114 Bilag A 347 r e = placePieceMax
Page 126 and 127:
116 Bilag A 443 r e = placePieceMax
Page 128 and 129:
118 Bilag A 537 b [ c +1][ r −1]
Page 130 and 131:
120 Bilag A 633 b [ c −1][ r −1
Page 132 and 133:
122 Bilag A 729 beta = r e [ 2 ] ;
Page 134 and 135:
124 Bilag A 825 beta = r e [ 2 ] ;
Page 136 and 137:
126 Bilag A 916 i f ( n . a > v ) 9
Page 138 and 139:
128 Bilag A 997 Root . wc=0; // tMo
Page 140 and 141:
130 Bilag A 1097 n . bc = tModel .
Page 142 and 143:
132 Bilag A 93 i f ( rowEnd >= tMod
Page 144 and 145:
134 Bilag A 192 alpha = n . a ; 193
Page 146 and 147:
136 Bilag A 291 } 292 293 // Min−
Page 148 and 149:
138 Bilag A 391 n . c h i l d r e n
Page 150 and 151:
140 Bilag A 493 i f ( Root . c h i
Page 152 and 153:
142 Bilag A 593 r e t u r n ( n ) ;
Page 154 and 155:
144 Bilag A 90 b [ c ] [ r ] = 0 ;
Page 156 and 157:
146 Bilag A 185 // System . out . p
Page 158 and 159:
148 Bilag A 280 } 281 282 // f l y
Page 160 and 161:
150 Bilag A 377 // i f ( tTree . ma
Page 162 and 163:
152 Bilag A 481 } 482 r e t u r n (
Page 164 and 165:
154 Bilag A 532 // System . out . p
Page 166 and 167:
156 Bilag A 33 p u b l i c i n t [
Page 168 and 169:
158 Bilag A 137 f o r ( i n t r =0;
Page 170 and 171:
160 Bilag A 245 f o r ( i n t r =0;
Page 172 and 173: 162 Bilag A 353 break ; 354 } 355 i
Page 174 and 175: 164 Bilag A 94 } 95 { // H o r i s
Page 176 and 177: 166 Bilag A 198 p r i v a t e void
Page 178 and 179: 168 Bilag A 303 f [ 1 ] [ 0 ] [ 1 ]
Page 180 and 181: 170 Bilag A 409 f o r ( i n t r =0;
Page 182 and 183: 172 Bilag A 43 p u b l i c Node cre
Page 184 and 185: 174 Bilag A 27 whiteScore = new Sco
Page 186 and 187: 176 Bilag A 65 bc = 1 ; 66 e l s e
Page 188 and 189: 178 Bilag A 58 p u b l i c void mou
Page 190 and 191: 180 Bilag A 149 S t r i n g t x t =
Page 192 and 193: 182 Bilag A l o a d i n g the f i l
Page 194 and 195: 184 Bilag A 7 p u b l i c AITaijiMi
Page 196 and 197: 186 Bilag A 105 i f ( b l a c k P l
Page 198 and 199: 188 Bilag A 211 tBoard . s e t P i
Page 200 and 201: 190 Bilag A 315 { 316 f o r ( i n t
Page 202 and 203: 192 Bilag A 416 { 417 //Bunden 418
Page 204 and 205: 194 Bilag A 510 p r i v a t e boole
Page 206 and 207: 196 Bilag A 612 } 613 614 // b e r
Page 208 and 209: 198 Bilag A 702 } 703 704 // s a e
Page 210 and 211: 200 Bilag A 802 p u b l i c void ne
Page 212 and 213: 202 Bilag A 59 g . f i l l R e c t
Page 214 and 215: 204 Bilag A 159 { 160 t h i s . set
Page 216 and 217: 206 Bilag A 81 System . out . p r i
Page 218 and 219: 208 Bilag A [ 1 ] [ 7 ] + ” ”+n
Page 220 and 221: 210 Bilag A 176 System . out . p r
Page 226 and 227: 216 Bilag A [ 8 ] [ 2 ] ) ; 277 i f
Page 228 and 229: 218 Bilag A 326 p u b l i c void pr
Page 230 and 231: 220 Bilag A 393 i f ( tModel . noRo
Page 232 and 233: 222 Bilag A 449 i f ( tModel . noRo
Page 236 and 237: 226 Bilag A 30 31 tFrame = frame ;
Page 238 and 239: 228 Bilag A 135 tFrame . tModel . s
Page 240 and 241: 230
Page 242 and 243: 232 Bilag B ”Introduction to Algo
Page 244: 234
show all

Kunstig Intelligens til Brætspillet Taiji - Danmarks Tekniske Universitet

Create successful ePaper yourself

Delete template?

Save as template?