Kunstig Intelligens til Brætspillet Taiji - Danmarks Tekniske Universitet

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210 Bilag A 176 System . out . p r i n t l n ( i +” ”+n . c h i l d r e n . get ( i )+” Depth : ”+n . c h i l d r e n . get ( i ) . d+” Score ”+n . c h i l d r e n . get ( i ) . a ) ; 177 System . out . p r i n t l n ( n . c h i l d r e n . get ( i ) . par+” ”+n . c h i l d r e n . get ( i ) . a+” b ”+n . c h i l d r e n . get ( i ) . bc+”,”+n . c h i l d r e n . get ( i ) . br+” w ”+n . c h i l d r e n . get ( i ) . wc+”,”+n . c h i l d r e n . get ( i ) . wr+” ”+n . c h i l d r e n . get ( i ) . c h i l d r e n +” ”+n . c h i l d r e n . get ( i ) . nodeBoard ) ; 178 System . out . p r i n t l n ( n . c h i l d r e n . get ( i ) . nodeBoard [ 0 ] [ 0 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 1 ] [ 0 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 2 ] [ 0 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 3 ] [ 0 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 4 ] [ 0 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 5 ] [ 0 ] ) ; 179 System . out . p r i n t l n ( n . c h i l d r e n . get ( i ) . nodeBoard [ 0 ] [ 1 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 1 ] [ 1 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 2 ] [ 1 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 3 ] [ 1 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 4 ] [ 1 ] + ” ”+n . c h i l d r e n . get ( i ) . nodeBoard [ 5 ] [ 1 ] ) ; 180 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 ] ) ; 181 i f ( tModel . noRows > 3) { 182 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 ] ) ; 183 i f ( tModel . noRows > 4) { 184 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 ] ) ; 185 i f ( tModel . noRows > 5) { 186 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 ] ) ; 187 i f ( tModel . noRows > 6) { 188 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 ] ) ; 189 i f ( tModel . noRows > 7) {
A.15 <strong>Taiji</strong>Print.java 211 190 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 ] ) ; 191 i f ( tModel . noRows > 8) { 192 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 ] ) ; 193 194 } 195 } 196 } 197 } 198 } 199 } 200 System . out . p r i n t l n ( ) ; 201 } 202 } 203 204 // f o r b r a e t med 7 k o l o n e r 205 p u b l i c void p r i n t C h i l d r e n 7 x ( Node n ) { 206 System . out . p r i n t l n (” t P r i n t P r i n t Children ”+n ) ; 207 f o r ( i n t i =0; i
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Kunstig Intelligens til Brætspille
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Summary This project concerns the d
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Forord Dette speciale er udarbejdet
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viii INDHOLD 5.3 Hash funktioner .
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2 Taiji Figur 1.1: Standard Taiji b
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4 Taiji 1.1.1 Forskelle mellem de o
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6 Taiji Alts˚a kan der maksimalt f
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8 Taiji Figur 1.9: Et standard Taij
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10 Taiji Der er dog en række speci
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12 Taiji Figur 1.15: Eksempel p˚a
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14 Taiji 1.4 Spillets kompleksitet
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16 Taiji Figur 1.18: Alle trækmuli
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18 Taiji Figur 1.21: Denne figur vi
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20 Taiji
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22 Design og brugervenlighed Figur
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24 Design og brugervenlighed naturl
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26 Design og brugervenlighed muligh
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28 Design og brugervenlighed 2.2.2
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30 Design og brugervenlighed uprakt
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32 Kunstig Intelligens 3.1.1 TaijiD
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34 Kunstig Intelligens 3.1.10 Figur
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36 Kunstig Intelligens 3.2.1 Nodes:
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38 Kunstig Intelligens at udvide de
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40 Minimax først søgningen, da de
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42 Minimax 4.3 Spilgraf for 3x3 Tai
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44 Minimax Herunder ses spilgrafen
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46 Minimax Herunder ses spilgrafen
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48 Minimax Som det kan ses ender sp
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50 Minimax Det kan ses at alle de e
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52 Optimering af Minimax Figur 5.1:
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56 Optimering af Minimax som et uni
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58 Optimering af Minimax Det er der
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60 Optimering af Minimax klarer sig
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62 Optimering af Minimax IF v < m T
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64 Optimering af Minimax er tilfæl
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68 Optimering af Minimax vigtigt at
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70 Optimering af Minimax
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72 Begrænsning af antallet af unde
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78 Test og sammenligning af de impl
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92 Konklusion selv n˚a igennem et
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94 Konklusion
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96 Bilag A 19 20 // i n i t i a l i
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98 Bilag A 120 nodes [ p [ 0 ] ] [
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100 Bilag A 218 i f ( n . a > beta
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102 Bilag A 319 p [2]= nodes [ p [
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104 Bilag A 419 420 421 422 423 424
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106 Bilag A 517 n . wr = tModel . n
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108 Bilag A 61 } 62 } 63 64 // c h
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110 Bilag A 155 b [ c +1][ r −1]
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112 Bilag A 251 b [ c −1][ r −1
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114 Bilag A 347 r e = placePieceMax
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116 Bilag A 443 r e = placePieceMax
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118 Bilag A 537 b [ c +1][ r −1]
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120 Bilag A 633 b [ c −1][ r −1
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122 Bilag A 729 beta = r e [ 2 ] ;
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124 Bilag A 825 beta = r e [ 2 ] ;
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126 Bilag A 916 i f ( n . a > v ) 9
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128 Bilag A 997 Root . wc=0; // tMo
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130 Bilag A 1097 n . bc = tModel .
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132 Bilag A 93 i f ( rowEnd >= tMod
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134 Bilag A 192 alpha = n . a ; 193
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136 Bilag A 291 } 292 293 // Min−
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138 Bilag A 391 n . c h i l d r e n
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140 Bilag A 493 i f ( Root . c h i
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142 Bilag A 593 r e t u r n ( n ) ;
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144 Bilag A 90 b [ c ] [ r ] = 0 ;
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146 Bilag A 185 // System . out . p
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148 Bilag A 280 } 281 282 // f l y
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150 Bilag A 377 // i f ( tTree . ma
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152 Bilag A 481 } 482 r e t u r n (
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154 Bilag A 532 // System . out . p
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156 Bilag A 33 p u b l i c i n t [
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158 Bilag A 137 f o r ( i n t r =0;
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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 222 and 223: 212 Bilag A 212 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
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Page 242 and 243: 232 Bilag B ”Introduction to Algo
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Kunstig Intelligens til Brætspillet Taiji - Danmarks Tekniske Universitet

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