ó .ŽKQc@sèddlmZddlmZddlZddlZddlmZdefd„ƒYZd„Zdefd „ƒYZd efd „ƒYZ d efd „ƒYZ defd„ƒYZ d„Z e Z defd„ƒYZdS(iÿÿÿÿ(tmanhattanDistance(t DirectionsN(tAgentt ReflexAgentcBs eZdZd„Zd„ZRS(s A reflex agent chooses an action at each choice point by examining its alternatives via a state evaluation function. The code below is provided as a guide. You are welcome to change it in any way you see fit, so long as you don't touch our method headers. c Cs‰|jƒ}g|D]}|j||ƒ^q}t|ƒ}gtt|ƒƒD]}|||krP|^qP}tj|ƒ}||S(s8 You do not need to change this method, but you're welcome to. getAction chooses among the best options according to the evaluation function. Just like in the previous project, getAction takes a GameState and returns some Directions.X for some X in the set {North, South, West, East, Stop} (tgetLegalActionstevaluationFunctiontmaxtrangetlentrandomtchoice( tselft gameStatet legalMovestactiontscorest bestScoretindext bestIndicest chosenIndex((shC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\00 Unofficial\02 multiagent\multiAgents.pyt getActions % 5cCsë|j|ƒ}|jƒ}|jƒ}|jƒ}g|D]}|j^q:}|jƒ} |jƒ} |jƒ} g|D]}|j^qz} |jƒjƒ} tƒ}d}x%| D]}|j|ƒ||7}q·Wtƒ}x-| D]%}t j | |ƒ}|j|ƒqèWt j |ƒ}d}x|D]}t ||ƒ}q-Wtƒ}x1| D])}dt j | |ƒ}|j|ƒqVWt j |ƒ}d}x|D]}t ||ƒ}qŸW|jƒ||}dG|GHdG|GHdG|GH|S(sc Design a better evaluation function here. The evaluation function takes in the current and proposed successor GameStates (pacman.py) and returns a number, where higher numbers are better. The code below extracts some useful information from the state, like the remaining food (oldFood) and Pacman position after moving (newPos). newScaredTimes holds the number of moves that each ghost will remain scared because of Pacman having eaten a power pellet. Print out these variables to see what you're getting, then combine them to create a masterful evaluation function. ii?Bigñh㈵øä>sclosestGhost: s closestFood: sscore: (tgeneratePacmanSuccessortgetPacmanPositiontgetFoodtgetGhostStatest scaredTimertgetGhostPositionstasListtlisttappendtutilRt normalizetminRtgetScore(R tcurrentGameStateRtsuccessorGameStatetnewPostoldFoodtnewGhostStatest ghostStatetnewScaredTimestpacmanPositiont ghostStatestghostPositionst scaredTimest foodPositionstghostScaredTimesttotalScaredTimet scaredTimetghostDistancest ghostPositiont ghostDistancet closestGhostt foodDistancest foodPositiont foodDistancet closestFoodtscore((shC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\00 Unofficial\02 multiagent\multiAgents.pyR0sH                  (t__name__t __module__t__doc__RR(((shC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\00 Unofficial\02 multiagent\multiAgents.pyRs cCs |jƒS(sï This default evaluation function just returns the score of the state. The score is the same one displayed in the Pacman GUI. This evaluation function is meant for use with adversarial search agents (not reflex agents). (R!(R"((shC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\00 Unofficial\02 multiagent\multiAgents.pytscoreEvaluationFunctionxstMultiAgentSearchAgentcBseZdZddd„ZRS(sE This class provides some common elements to all of your multi-agent searchers. Any methods defined here will be available to the MinimaxPacmanAgent, AlphaBetaPacmanAgent & ExpectimaxPacmanAgent. You *do not* need to make any changes here, but you can if you want to add functionality to all your adversarial search agents. Please do not remove anything, however. Note: this is an abstract class: one that should not be instantiated. It's only partially specified, and designed to be extended. Agent (game.py) is another abstract class. R=t2cCs4d|_tj|tƒƒ|_t|ƒ|_dS(Ni(RRtlookuptglobalsRtinttdepth(R tevalFnRC((shC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\00 Unofficial\02 multiagent\multiAgents.pyt__init__‘s (R:R;R<RE(((shC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\00 Unofficial\02 multiagent\multiAgents.pyR>‚s t MinimaxAgentcBseZdZd„ZRS(s' Your minimax agent (question 2) cCstjƒdS(s Returns the minimax action from the current gameState using self.depth and self.evaluationFunction. Here are some method calls that might be useful when implementing minimax. gameState.getLegalActions(agentIndex): Returns a list of legal actions for an agent agentIndex=0 means Pacman, ghosts are >= 1 Directions.STOP: The stop direction, which is always legal gameState.generateSuccessor(agentIndex, action): Returns the successor game state after an agent takes an action gameState.getNumAgents(): Returns the total number of agents in the game N(RtraiseNotDefined(R R ((shC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\00 Unofficial\02 multiagent\multiAgents.pyR›s(R:R;R<R(((shC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\00 Unofficial\02 multiagent\multiAgents.pyRF–stAlphaBetaAgentcBseZdZd„ZRS(s? Your minimax agent with alpha-beta pruning (question 3) cCstjƒdS(sS Returns the minimax action using self.depth and self.evaluationFunction N(RRG(R R ((shC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\00 Unofficial\02 multiagent\multiAgents.pyR·s(R:R;R<R(((shC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\00 Unofficial\02 multiagent\multiAgents.pyRH²stExpectimaxAgentcBseZdZd„ZRS(s* Your expectimax agent (question 4) cCstjƒdS(s¸ Returns the expectimax action using self.depth and self.evaluationFunction All ghosts should be modeled as choosing uniformly at random from their legal moves. N(RRG(R R ((shC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\00 Unofficial\02 multiagent\multiAgents.pyRÃs(R:R;R<R(((shC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\00 Unofficial\02 multiagent\multiAgents.pyRI¾scCstjƒdS(sµ Your extreme ghost-hunting, pellet-nabbing, food-gobbling, unstoppable evaluation function (question 5). DESCRIPTION: N(RRG(R"((shC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\00 Unofficial\02 multiagent\multiAgents.pytbetterEvaluationFunctionÍst ContestAgentcBseZdZd„ZRS(s' Your agent for the mini-contest cCstjƒdS(s| Returns an action. You can use any method you want and search to any depth you want. Just remember that the mini-contest is timed, so you have to trade off speed and computation. Ghosts don't behave randomly anymore, but they aren't perfect either -- they'll usually just make a beeline straight towards Pacman (or away from him if they're scared!) N(RRG(R R ((shC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\00 Unofficial\02 multiagent\multiAgents.pyRßs (R:R;R<R(((shC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\00 Unofficial\02 multiagent\multiAgents.pyRKÚs(RRtgameRR RRR=R>RFRHRIRJtbetterRK(((shC:\Users\user\Desktop\Studies\02 BerkeleyX cs188x\05 Projects\00 Unofficial\02 multiagent\multiAgents.pyt si