Kunstig Intelligens til Brætspillet Taiji - Danmarks Tekniske Universitet

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152 Bilag A 481 } 482 r e t u r n ( n ) ; 483 } 484 485 486 p r i v a t e void p r i n t L e a v e s ( ) { 487 System . out . p r i n t l n (” Current Leaves ”) ; 488 f o r ( i n t i =0; i
A.4 AI<strong>Taiji</strong>Minimax.java 153 [ 1 ] [ 2 ] + ” ”+tTree . evenGen . get ( i ) . nodeBoard [ 2 ] [ 2 ] ) ; 506 System . out . p r i n t l n ( ) ; 507 } 508 } 509 510 p r i v a t e void printOddGen ( ) { 511 System . out . p r i n t l n (”OddGen”) ; 512 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
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 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 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 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
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202 Bilag A 59 g . f i l l R e c t
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204 Bilag A 159 { 160 t h i s . set
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206 Bilag A 81 System . out . p r i
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208 Bilag A [ 1 ] [ 7 ] + ” ”+n
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210 Bilag A 176 System . out . p r
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216 Bilag A [ 8 ] [ 2 ] ) ; 277 i f
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218 Bilag A 326 p u b l i c void pr
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220 Bilag A 393 i f ( tModel . noRo
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222 Bilag A 449 i f ( tModel . noRo
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226 Bilag A 30 31 tFrame = frame ;
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228 Bilag A 135 tFrame . tModel . s
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230
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232 Bilag B ”Introduction to Algo
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234
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Kunstig Intelligens til Brætspillet Taiji - Danmarks Tekniske Universitet

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