# inference.py # ------------ # Licensing Information: Please do not distribute or publish solutions to this # project. You are free to use and extend these projects for educational # purposes. The Pacman AI projects were developed at UC Berkeley, primarily by # John DeNero (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu). # For more info, see http://inst.eecs.berkeley.edu/~cs188/sp09/pacman.html import util import random import busters import game class InferenceModule: """ An inference module tracks a belief distribution over a ghost's location. This is an abstract class, which you should not modify. """ ############################################ # Useful methods for all inference modules # ############################################ def __init__(self, ghostAgent): "Sets the ghost agent for later access" self.ghostAgent = ghostAgent self.index = ghostAgent.index def getPositionDistribution(self, gameState): """ Returns a distribution over successor positions of the ghost from the given gameState. You must first place the ghost in the gameState, using setGhostPosition below. """ ghostPosition = gameState.getGhostPosition(self.index) # The position you set actionDist = self.ghostAgent.getDistribution(gameState) dist = util.Counter() for action, prob in actionDist.items(): successorPosition = game.Actions.getSuccessor(ghostPosition, action) dist[successorPosition] = prob return dist def setGhostPosition(self, gameState, ghostPosition): """ Sets the position of the ghost for this inference module to the specified position in the supplied gameState. """ conf = game.Configuration(ghostPosition, game.Directions.STOP) gameState.data.agentStates[self.index] = game.AgentState(conf, False) return gameState def observeState(self, gameState): "Collects the relevant noisy distance observation and pass it along." distances = gameState.getNoisyGhostDistances() if len(distances) >= self.index: # Check for missing observations obs = distances[self.index - 1] self.observe(obs, gameState) def initialize(self, gameState): "Initializes beliefs to a uniform distribution over all positions." # The legal positions do not include the ghost prison cells in the bottom left. self.legalPositions = [p for p in gameState.getWalls().asList(False) if p[1] > 1] self.initializeUniformly(gameState) ###################################### # Methods that need to be overridden # ###################################### def initializeUniformly(self, gameState): "Sets the belief state to a uniform prior belief over all positions." pass def observe(self, observation, gameState): "Updates beliefs based on the given distance observation and gameState." pass def elapseTime(self, gameState): "Updates beliefs for a time step elapsing from a gameState." pass def getBeliefDistribution(self): """ Returns the agent's current belief state, a distribution over ghost locations conditioned on all evidence so far. """ pass class ExactInference(InferenceModule): """ The exact dynamic inference module should use forward-algorithm updates to compute the exact belief function at each time step. """ def initializeUniformly(self, gameState): "Begin with a uniform distribution over ghost positions." self.beliefs = util.Counter() for p in self.legalPositions: self.beliefs[p] = 1.0 self.beliefs.normalize() def observe(self, observation, gameState): """ Updates beliefs based on the distance observation and Pacman's position. The noisyDistance is the estimated manhattan distance to the ghost you are tracking. The emissionModel below stores the probability of the noisyDistance for any true distance you supply. That is, it stores P(noisyDistance | TrueDistance). self.legalPositions is a list of the possible ghost positions (you should only consider positions that are in self.legalPositions). """ noisyDistance = observation emissionModel = busters.getObservationDistribution(noisyDistance) pacmanPosition = gameState.getPacmanPosition() "*** YOUR CODE HERE ***" # Replace this code with a correct observation update allPossible = util.Counter() for p in self.legalPositions: trueDistance = util.manhattanDistance(p, pacmanPosition) if emissionModel[trueDistance] > 0: allPossible[p] = 1.0 allPossible.normalize() "*** YOUR CODE HERE ***" self.beliefs = allPossible def elapseTime(self, gameState): """ Update self.beliefs in response to a time step passing from the current state. The transition model is not entirely stationary: it may depend on Pacman's current position (e.g., for DirectionalGhost). However, this is not a problem, as Pacman's current position is known. In order to obtain the distribution over new positions for the ghost, given its previous position (oldPos) as well as Pacman's current position, use this line of code: newPosDist = self.getPositionDistribution(self.setGhostPosition(gameState, oldPos)) Note that you may need to replace "oldPos" with the correct name of the variable that you have used to refer to the previous ghost position for which you are computing this distribution. newPosDist is a util.Counter object, where for each position p in self.legalPositions, newPostDist[p] = Pr( ghost is at position p at time t + 1 | ghost is at position oldPos at time t ) (and also given Pacman's current position). You may also find it useful to loop over key, value pairs in newPosDist, like: for newPos, prob in newPosDist: ... As an implementation detail (with which you need not concern yourself), the line of code above for obtaining newPosDist makes use of two helper methods provided in InferenceModule above: 1) self.setGhostPosition(gameState, ghostPosition) This method alters the gameState by placing the ghost we're tracking in a particular position. This altered gameState can be used to query what the ghost would do in this position. 2) self.getPositionDistribution(gameState) This method uses the ghost agent to determine what positions the ghost will move to from the provided gameState. The ghost must be placed in the gameState with a call to self.setGhostPosition above. """ "*** YOUR CODE HERE ***" def getBeliefDistribution(self): return self.beliefs class ParticleFilter(InferenceModule): """ A particle filter for approximately tracking a single ghost. Useful helper functions will include random.choice, which chooses an element from a list uniformly at random, and util.sample, which samples a key from a Counter by treating its values as probabilities. """ def initializeUniformly(self, gameState, numParticles=300): "Initializes a list of particles." self.numParticles = numParticles "*** YOUR CODE HERE ***" def observe(self, observation, gameState): "Update beliefs based on the given distance observation." emissionModel = busters.getObservationDistribution(observation) pacmanPosition = gameState.getPacmanPosition() "*** YOUR CODE HERE ***" util.raiseNotDefined() def elapseTime(self, gameState): """ Update beliefs for a time step elapsing. As in the elapseTime method of ExactInference, you should use: newPosDist = self.getPositionDistribution(self.setGhostPosition(gameState, oldPos)) to obtain the distribution over new positions for the ghost, given its previous position (oldPos) as well as Pacman's current position. """ "*** YOUR CODE HERE ***" util.raiseNotDefined() def getBeliefDistribution(self): """ Return the agent's current belief state, a distribution over ghost locations conditioned on all evidence and time passage. """ "*** YOUR CODE HERE ***" util.raiseNotDefined() class MarginalInference(InferenceModule): "A wrapper around the JointInference module that returns marginal beliefs about ghosts." def initializeUniformly(self, gameState): "Set the belief state to an initial, prior value." if self.index == 1: jointInference.initialize(gameState, self.legalPositions) jointInference.addGhostAgent(self.ghostAgent) def observeState(self, gameState): "Update beliefs based on the given distance observation and gameState." if self.index == 1: jointInference.observeState(gameState) def elapseTime(self, gameState): "Update beliefs for a time step elapsing from a gameState." if self.index == 1: jointInference.elapseTime(gameState) def getBeliefDistribution(self): "Returns the marginal belief over a particular ghost by summing out the others." jointDistribution = jointInference.getBeliefDistribution() dist = util.Counter() for t, prob in jointDistribution.items(): dist[t[self.index - 1]] += prob return dist class JointParticleFilter: "JointParticleFilter tracks a joint distribution over tuples of all ghost positions." def initialize(self, gameState, legalPositions, numParticles = 600): "Stores information about the game, then initializes particles." self.numGhosts = gameState.getNumAgents() - 1 self.numParticles = numParticles self.ghostAgents = [] self.legalPositions = legalPositions self.initializeParticles() def initializeParticles(self): "Initializes particles randomly. Each particle is a tuple of ghost positions." self.particles = [] for i in range(self.numParticles): self.particles.append(tuple([random.choice(self.legalPositions) for j in range(self.numGhosts)])) def addGhostAgent(self, agent): "Each ghost agent is registered separately and stored (in case they are different)." self.ghostAgents.append(agent) def elapseTime(self, gameState): """ Samples each particle's next state based on its current state and the gameState. To loop over the ghosts, use: for i in range(self.numGhosts): ... Then, assuming that "i" refers to the (0-based) index of the ghost, to obtain the distributions over new positions for that single ghost, given the list (prevGhostPositions) of previous positions of ALL of the ghosts, use this line of code: newPosDist = getPositionDistributionForGhost(setGhostPositions(gameState, prevGhostPositions), i + 1, self.ghostAgents[i]) Note that you may need to replace "prevGhostPositions" with the correct name of the variable that you have used to refer to the list of the previous positions of all of the ghosts, and you may need to replace "i" with the variable you have used to refer to the index of the ghost for which you are computing the new position distribution. As an implementation detail (with which you need not concern yourself), the line of code above for obtaining newPosDist makes use of two helper functions defined below in this file: 1) setGhostPositions(gameState, ghostPositions) This method alters the gameState by placing the ghosts in the supplied positions. 2) getPositionDistributionForGhost(gameState, ghostIndex, agent) This method uses the supplied ghost agent to determine what positions a ghost (ghostIndex) controlled by a particular agent (ghostAgent) will move to in the supplied gameState. All ghosts must first be placed in the gameState using setGhostPositions above. Remember: ghosts start at index 1 (Pacman is agent 0). The ghost agent you are meant to supply is self.ghostAgents[ghostIndex-1], but in this project all ghost agents are always the same. """ newParticles = [] for oldParticle in self.particles: newParticle = list(oldParticle) # A list of ghost positions "*** YOUR CODE HERE ***" newParticles.append(tuple(newParticle)) self.particles = newParticles def observeState(self, gameState): """ Resamples the set of particles using the likelihood of the noisy observations. As in elapseTime, to loop over the ghosts, use: for i in range(self.numGhosts): ... A correct implementation will handle two special cases: 1) When a ghost is captured by Pacman, all particles should be updated so that the ghost appears in its prison cell, position (2 * i + 1, 1), where "i" is the 0-based index of the ghost. You can check if a ghost has been captured by Pacman by checking if it has a noisyDistance of 999 (a noisy distance of 999 will be returned if, and only if, the ghost is captured). 2) When all particles receive 0 weight, they should be recreated from the prior distribution by calling initializeParticles. """ pacmanPosition = gameState.getPacmanPosition() noisyDistances = gameState.getNoisyGhostDistances() if len(noisyDistances) < self.numGhosts: return emissionModels = [busters.getObservationDistribution(dist) for dist in noisyDistances] "*** YOUR CODE HERE ***" def getBeliefDistribution(self): dist = util.Counter() for part in self.particles: dist[part] += 1 dist.normalize() return dist # One JointInference module is shared globally across instances of MarginalInference jointInference = JointParticleFilter() def getPositionDistributionForGhost(gameState, ghostIndex, agent): """ Returns the distribution over positions for a ghost, using the supplied gameState. """ ghostPosition = gameState.getGhostPosition(ghostIndex) actionDist = agent.getDistribution(gameState) dist = util.Counter() for action, prob in actionDist.items(): successorPosition = game.Actions.getSuccessor(ghostPosition, action) dist[successorPosition] = prob return dist def setGhostPositions(gameState, ghostPositions): "Sets the position of all ghosts to the values in ghostPositionTuple." for index, pos in enumerate(ghostPositions): conf = game.Configuration(pos, game.Directions.STOP) gameState.data.agentStates[index + 1] = game.AgentState(conf, False) return gameState