agents/hunter/hunter.py

   1 #!/usr/bin/python -u
   2
   3 #NOTE: The -u option is required for unbuffered stdin/stdout.
   4 #       If stdin/stdout are buffered, the manager program will not recieve any messages and assume that the agent has timed out.
   5
   6 '''
   7  khaos.py - A sample Stratego AI for the UCC Programming Competition 2012
   8
   9  The name describes the state of this file :S
  10
  11  Written in python, the slithery language
  12
  13  author Sam Moore (matches) [SZM]
  14  website http://matches.ucc.asn.au/stratego
  15  email [email protected] or [email protected]
  16  git git.ucc.asn.au/progcomp2012.git
  17 '''
  18
  19 import os
  20
  21 from basic_python import *
  22 from path import *
  23
  24 def OppositeDirection(direction):
  25         if direction == "UP":
  26                 return "DOWN"
  27         elif direction == "DOWN":
  28                 return "UP"
  29         elif direction == "LEFT":
  30                 return "RIGHT"
  31         elif direction == "RIGHT":
  32                 return "LEFT"
  33         else:
  34                 assert(False)
  35         return "ERROR"
  36
  37 class Hunter(BasicAI):
  38         " Python based AI of DEATH "
  39         def __init__(self, scoresFilename=None):
  40                 if scoresFilename == None:
  41                         scoresFilename = "default.scores"
  42                 BasicAI.__init__(self)
  43
  44                 scoresFile = open(scoresFilename, "r")
  45                 self.scoreTable = []
  46                 for i in scoresFile.readline().strip().split(' '):
  47                         self.scoreTable.append(float(i))
  48                 scoresFile.close()
  49
  50                 self.maxdepth = 1
  51                 self.recursiveConsider = {"allies" : 5, "enemies" : 5}
  52
  53
  54
  55         def PositionLegal(self, x, y, unit = None):
  56                 if x >= 0 and x < len(self.board) and y >= 0 and y < len(self.board[x]):
  57                         if unit == None:
  58                                 return True
  59                         else:
  60                                 return self.board[x][y] == None or self.board[x][y].colour == oppositeColour(unit.colour)
  61                 else:
  62                         return False
  63
  64         def BestMove(self, maxdepth = 1):
  65
  66                 moveList = []
  67
  68
  69                 if maxdepth < self.maxdepth:
  70                         #sys.stderr.write("Recurse!\n")
  71                         considerAllies = self.recursiveConsider["allies"]
  72                         considerEnemies = self.recursiveConsider["enemies"]
  73                 else:
  74                         considerAllies = len(self.units)+1
  75                         considerEnemies = len(self.enemyUnits)+1
  76
  77                 for enemy in self.enemyUnits[0:considerEnemies]:
  78                         for ally in self.units[0:considerAllies]:
  79                                 moveList.append(self.DesiredMove(ally, enemy))
  80
  81                 for desiredMove in moveList:
  82                         if desiredMove[0] == "NO_MOVE" or desiredMove[2] == None:
  83                                 desiredMove[1] = -2.0
  84
  85
  86
  87
  88                 if maxdepth > 1:
  89                         for desiredMove in moveList:
  90                                 if desiredMove[2] == None or desiredMove[1] < 0.0:
  91                                         continue
  92                                 p = move(desiredMove[3].x, desiredMove[3].y, desiredMove[2][0], 1)
  93                                 if self.board[p[0]][p[1]] == None:
  94                                         x = desiredMove[3].x
  95                                         y = desiredMove[3].y
  96                                         result = desiredMove[0] + " OK"
  97                                         self.InterpretResult(result)
  98                                         bestRecurse = self.BestMove(maxdepth-1)
  99                                         if bestRecurse != None:
 100                                                 desiredMove[1] += bestRecurse[1]# / float(max(1.0, maxdepth))
 101                                         self.board[desiredMove[3].x][desiredMove[3].y] = None
 102                                         self.board[x][y] = desiredMove[3]
 103                                         desiredMove[3].x = x
 104                                         desiredMove[3].y = y
 105
 106                 for desiredMove in moveList:
 107                         if desiredMove[1] > 0.0:
 108                                 desiredMove[1] = desiredMove[1] / float(len(desiredMove[2]))
 109
 110                 if len(moveList) <= 0:
 111                         return None
 112                 moveList.sort(key = lambda e : e[1], reverse = True)
 113                 return moveList[0]
 114
 115
 116         def DesiredMove(self, ally, enemy):
 117                 """ Determine desired move of allied piece, towards or away from enemy, with score value """
 118                 scaleFactor = 1.0
 119                 if ally.rank == 'F' or ally.rank == 'B':
 120                         return ["NO_MOVE", 0, None, ally, enemy]
 121
 122                 actionScores = {"ATTACK" : 0, "RETREAT" : 0}
 123                 if enemy.rank == '?':
 124                         for i in range(0, len(ranks)):
 125                                 prob = self.rankProbability(enemy, ranks[i])
 126                                 if prob > 0:
 127                                         desiredAction = self.DesiredAction(ally, ranks[i])
 128                                         actionScores[desiredAction[0]] += prob* (desiredAction[1] / 2.0)
 129                         if len(enemy.positions) <= 1 and ally.rank != '8':
 130                                 scaleFactor *= (1.0 - float(valuedRank(ally.rank)) / float(valuedRank('1')))**2.0
 131                         elif len(enemy.positions) > 1 and ally.rank == '8':
 132                                 scaleFactor *= 0.05
 133                         #elif len(enemy.positions) > 1:
 134                         #       scaleFactor *= (1.0 - float(valuedRank(ally.rank)) / float(valuedRank('1')))**0.25
 135                         #       scaleFactor = max(0.05, scaleFactor)
 136                 else:
 137                         desiredAction = self.DesiredAction(ally, enemy.rank)
 138                         actionScores[desiredAction[0]] += desiredAction[1]
 139
 140
 141                 desiredAction = sorted(actionScores.items(), key = lambda e : e[1], reverse = True)[0]
 142                 direction = None
 143                 #path = PathFinder().pathFind((ally.x, ally.y), (enemy.x, enemy.y), self.board)
 144
 145                 #if path != False and len(path) > 0:
 146                 #       if desiredAction[0] == "RETREAT":
 147                                 #sys.stderr.write("Recommend retreat! "+ally.rank + " from " + enemy.rank+"\n")
 148                 #               direction = OppositeDirection(path[0])
 149                 #               p = move(ally.x, ally.y, direction, 1)
 150                 #               if self.PositionLegal(p[0], p[1], ally) == False:
 151                 #                       path = None
 152                 #               scaleFactor = 0.05 * scaleFactor
 153                 #       else:
 154                 #               direction = path[0]
 155
 156                 #       return [str(ally.x) + " " + str(ally.y) + " " + direction, desiredAction[1] * scaleFactor, path, ally, enemy]
 157
 158                 directions = {"RIGHT" : enemy.x - ally.x, "LEFT" : ally.x - enemy.x, "DOWN" : enemy.y - ally.y, "UP" : ally.y - enemy.y}
 159                 if desiredAction[0] == "RETREAT":
 160                         for key in directions.keys():
 161                                 directions[key] = -directions[key]
 162
 163                 while direction == None:
 164                         d = sorted(directions.items(), key = lambda e : e[1], reverse = True)
 165                         p = move(ally.x, ally.y, d[0][0], 1)
 166                         if self.PositionLegal(p[0], p[1]) and (self.board[p[0]][p[1]] == None or self.board[p[0]][p[1]] == enemy):
 167                                 direction = d[0][0]
 168                                 scaleFactor *= (1.0 - float(max(d[0][1], 0.0)) / 10.0)**2.0
 169                         else:
 170                                 del directions[d[0][0]]
 171                                 if len(directions.keys()) <= 0:
 172                                         break
 173
 174                 #if abs(enemy.x - ally.x) >= abs(enemy.y - ally.y):
 175                 #       if enemy.x > ally.x:
 176                 #               direction = "RIGHT"
 177                 #       elif enemy.x < ally.x:
 178                 #
 179                 #else:
 180                 #       if enemy.y > ally.y:
 181                 #               direction = "DOWN"
 182                 #       elif enemy.y < ally.y:
 183                 #               direction = "UP"
 184                 if direction == None:
 185                         return ["NO_MOVE", 0, [], ally, enemy]
 186                 return [str(ally.x) + " " + str(ally.y) + " " + direction, desiredAction[1], [direction], ally, enemy]
 187
 188
 189         def DesiredAction(self, ally, enemyRank):
 190                 if enemyRank == 'F':
 191                         return ["ATTACK", 1.0]
 192                 if ally.rank == '8' and enemyRank == 'B':
 193                         return ["ATTACK", 0.9]
 194                 if ally.rank == '1' and enemyRank == 's':
 195                         return ["RETREAT", 0.9]
 196                 if ally.rank == 's' and enemyRank == '1':
 197                         return ["ATTACK", 0.6]
 198                 if enemyRank == 'B':
 199                         return ["RETREAT", 0.0]
 200                 if ally.rank == enemyRank:
 201                         return ["ATTACK", 0.1]
 202                 if valuedRank(ally.rank) > valuedRank(enemyRank):
 203                         return ["ATTACK", float(self.scoreTable[ranks.index(enemyRank)]) * (0.1 + 1.0/float(self.scoreTable[ranks.index(ally.rank)]))]
 204                 else:
 205                         return ["RETREAT", float(self.scoreTable[ranks.index(ally.rank)]) / 10.0]
 206
 207
 208         def MakeMove(self):
 209                 if len(self.units) < 20:
 210                         self.maxdepth = 1
 211                 bestMove = self.BestMove(self.maxdepth)
 212
 213
 214                 if bestMove == None:
 215                         #sys.stderr.write("Khaos makes random move!\n")
 216                         return BasicAI.MakeMove(self)
 217
 218                 #sys.stderr.write("Board state before move: \n")
 219                 #self.debugPrintBoard()
 220
 221                 sys.stderr.write("Best move is \"" + bestMove[0] + "\" with score " +  str(bestMove[1]) + " as part of path " +str(bestMove[2]) + " ...\n")
 222                 sys.stderr.write("       Ally with rank " + bestMove[3].rank + " is targeting unit at " + str((bestMove[4].x, bestMove[4].y)) + " rank " + bestMove[4].rank + "\n")
 223                 sys.stdout.write(bestMove[0] + "\n")
 224
 225
 226                 return True
 227
 228
 229
 230         def rankProbability(self, target, targetRank):
 231
 232                 if targetRank == '+' or targetRank == '?':
 233                         return 0.0
 234                 if target.rank == targetRank:
 235                         return 1.0
 236                 elif target.rank != '?':
 237                         return 0.0
 238
 239                 total = 0.0
 240                 for rank in ranks:
 241                         if rank == '+' or rank == '?':
 242                                 continue
 243                         elif rank == 'F' or rank == 'B':
 244                                 if target.lastMoved < 0:
 245                                         total += self.hiddenEnemies[rank]
 246                         else:
 247                                 total += self.hiddenEnemies[rank]
 248
 249                 if total == 0.0:
 250                         return 0.0
 251                 return float(float(self.hiddenEnemies[targetRank]) / float(total))
 252
 253         def InterpretResult(self, string=None):
 254                 if BasicAI.InterpretResult(self, string) == False:
 255                         return False
 256
 257
 258                 if self.maxdepth > 1:
 259                         if self.lastMoved != None and self.lastMoved.colour == self.colour and self.lastMoved.alive == False:
 260                                 self.units.sort(key = lambda e : valuedRank(e.rank), reverse = True)
 261                         elif self.lastMoved != None and self.lastMoved.colour == oppositeColour(self.colour) and self.lastMoved.alive == True:
 262                                 oldRank = self.lastMoved.rank
 263                                 self.lastMoved.rank = '1'
 264                                 self.enemyUnits.sort(key = lambda e : valuedRank(e.rank), reverse = True)
 265                                 self.lastMoved.rank = oldRank
 266
 267
 268                 return True
 269
 270
 271 if __name__ == "__main__":
 272         if len(sys.argv) > 1:
 273                 hunter = Hunter(sys.argv[1])
 274         else:
 275                 string = ""
 276                 path = sys.argv[0].split('/')
 277                 for i in range(0, len(path)-1):
 278                         string += path[i] + "/"
 279                 string += "default.scores"
 280
 281
 282                 hunter = Hunter(string)
 283         if hunter.Setup():
 284                 while hunter.MoveCycle():
 285                         pass
 286