progcomp/agents/vixen/vixen.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  vixen.py - A sample Stratego AI for the UCC Programming Competition 2012
   8
   9  Written in python, the slithery language
  10
  11  author Sam Moore (matches) [SZM]
  12  website http://matches.ucc.asn.au/stratego
  13  email [email protected] or [email protected]
  14  git git.ucc.asn.au/progcomp2012.git
  15 '''
  16
  17 from basic_python import *
  18 from path import *
  19
  20
  21
  22 class Vixen(BasicAI):
  23         " Python based AI, improves upon Asmodeus by taking into account probabilities, and common paths "
  24         def __init__(self):
  25                 #sys.stderr.write("Vixen initialised...\n")
  26                 BasicAI.__init__(self)
  27
  28
  29                 self.bombScores = {'1' : -0.9 , '2' : -0.8 , '3' : -0.5 , '4' : 0.1, '5' : 0.1, '6' : 0.3, '7' : 0.7, '8' : 1 , '9' : 0.6, 's' : 0}
  30                 self.suicideScores = {'1' : -0.5 , '2' : -0.4 , '3' : -0.35, '4' : -0.25, '5' : -0.2, '6' : 0.0, '7' : 0.1, '8' : -0.4 , '9' : 0.0, 's' : -0.4}
  31                 self.killScores = {'1' : 1.0 , '2' : 0.9 , '3' : 0.9 , '4' : 0.8, '5' : 0.8, '6' : 0.8, '7' : 0.8, '8' : 0.9 , '9' : 0.7, 's' : 0.9}
  32                 self.riskScores = {'1' : 0.0, '2' : 0.1, '3' : 0.2, '4': 0.4, '5': 0.6, '6': 0.7, '7':0.8, '8': 0.0, '9' : 1.0, 's' : 0.1}
  33
  34
  35
  36
  37
  38         def MakeMove(self):
  39                 #sys.stderr.write("Vixen MakingMove...\n")
  40                 " Over-rides the default BasicAI.MakeMove function "
  41
  42                 moveList = []
  43                 for unit in self.units:
  44                         if unit.mobile() == False:
  45                                 continue
  46
  47                         scores = {"LEFT":0, "RIGHT":0, "UP":0, "DOWN":0}
  48
  49
  50                         for target in self.enemyUnits:
  51                                 if target == unit:
  52                                         continue
  53                                 path = PathFinder().pathFind((unit.x, unit.y), (target.x, target.y), self.board)
  54                                 if path == False or len(path) == 0:
  55                                         continue
  56                                 moveList.append({"unit":unit, "direction":path[0], "score":self.CalculateScore(unit, target, path)})
  57                                 #scores[path[0]] += self.CalculateScore(unit, target, path)
  58
  59                         #bestScore = sorted(scores.items(), key = lambda e : e[1], reverse=True)[0]
  60                         #moveList.append({"unit":unit, "direction":bestScore[0], "score":bestScore[1]})
  61
  62
  63                 if len(moveList) == 0:
  64                         print "NO_MOVE"
  65                         return True
  66
  67                 moveList.sort(key = lambda e : e["score"], reverse=True)
  68                 sys.stderr.write("vixen - best move: " + str(moveList[0]["unit"].x) + " " + str(moveList[0]["unit"].y) + " " + moveList[0]["direction"] + " [ score = " + str(moveList[0]["score"]) + " ]\n")
  69                 if moveList[0]["score"] == 0:
  70                         print "NO_MOVE"
  71                         return True
  72
  73
  74                 print str(moveList[0]["unit"].x) + " " + str(moveList[0]["unit"].y) + " " + moveList[0]["direction"]
  75                 return True
  76
  77
  78         def tailFactor(self, pathLength):
  79                 #if pathLength >= len(self.tailFactors) or pathLength <= 0:
  80                 #       return 0.0
  81                 #return self.tailFactors[pathLength]
  82                 #return 0.5 * (1.0 + pow(pathLength, 0.75))
  83                 return 1.0 / pathLength
  84
  85
  86         def CalculateScore(self, attacker, defender, path):
  87                 total = 0.0
  88                 count = 0.0
  89                 for rank in ranks:
  90                         prob = self.rankProbability(defender, rank)
  91                         if prob > 0.0:
  92                                 #sys.stderr.write("     " + str(attacker.rank) + " vs. " + str(rank) + " [" + str(prob) + "] score " + str(self.combatScore(attacker.rank, rank, len(path))) + "\n")
  93                                 total += prob * self.combatScore(attacker.rank, rank, len(path))
  94                                 count += 1
  95
  96
  97                 #if count > 1:
  98                 #       total = total / count + self.riskScore(attacker.rank)
  99
 100
 101                 total = total * self.tailFactor(len(path))
 102                 #sys.stderr.write("Total score for " + str(attacker) + " vs. " + str(defender) + " is " + str(total) + "\n")
 103                 return total
 104
 105         def combatScore(self, attackerRank, defenderRank, pathLength):
 106                 if defenderRank == 'F':
 107                         return 1.0
 108                 elif defenderRank == 'B':
 109                         return self.bombScore(attackerRank)
 110                 elif defenderRank == 's' and attackerRank == '1' and pathLength == 2:
 111                         return self.suicideScore(attackerRank)
 112                 elif defenderRank == '1' and attackerRank == 's' and pathLength != 2:
 113                         return self.killScore(attackerRank)
 114
 115                 if valuedRank(attackerRank) > valuedRank(defenderRank):
 116                         return self.killScore(defenderRank)
 117                 elif valuedRank(attackerRank) < valuedRank(defenderRank):
 118                         return self.suicideScore(attackerRank)
 119                 return self.killScore(defenderRank) + self.suicideScore(attackerRank)
 120
 121         def killScore(self, defenderRank):
 122                 return self.killScores[defenderRank]
 123
 124         def bombScore(self, attackerRank):
 125                 return self.bombScores[attackerRank]
 126
 127         def suicideScore(self, attackerRank):
 128                 return self.suicideScores[attackerRank]
 129
 130         def riskScore(self, attackerRank):
 131                 return self.riskScores[attackerRank]
 132
 133         def rankProbability(self, target, targetRank):
 134                 if targetRank == '+' or targetRank == '?':
 135                         return 0.0
 136                 if target.rank == targetRank:
 137                         return 1.0
 138                 elif target.rank != '?':
 139                         return 0.0
 140
 141                 total = 0.0
 142                 for rank in ranks:
 143                         if rank == '+' or rank == '?':
 144                                 continue
 145                         elif rank == 'F' or rank == 'B':
 146                                 if target.lastMoved < 0:
 147                                         total += self.hiddenEnemies[rank]
 148                         else:
 149                                 total += self.hiddenEnemies[rank]
 150
 151                 if total == 0.0:
 152                         return 0.0
 153                 return float(float(self.hiddenEnemies[targetRank]) / float(total))
 154
 155         def InterpretResult(self):
 156                 """ Over-ride the basic AI interpret result so we can update probabilities """
 157                 if BasicAI.InterpretResult(self) == False:
 158                         return False
 159
 160                 return True
 161
 162
 163
 164
 165
 166 if __name__ == "__main__":
 167         vixen = Vixen()
 168         if vixen.Setup():
 169                 while vixen.MoveCycle():
 170                         pass
 171