Assignment 5: Bayesian Networks’s SolutionPart 1 SolutionQuestion 2Part 2 Solution class ExactStaticInferenceModule(StaticInferenceModule): """ You will implement an exact inference module for the static battleship game. See the abstract 'StaticInferenceModule' class for descriptions of the methods. The current implementation below is broken, returning all uniform distributions. """ def getShipTupleDistributionGivenObservations(self, observations): # BEGIN SOLUTION p_ShipTuple_and_observations = Counter() p_ShipTuple = self.game.getShipTupleDistribution() for shipTuple in self.game.getShipTuples(): p = p_ShipTuple.getCount(shipTuple) for observation in observations.items(): sensor, reading = observation p_Reading_given_shipTuple = self.game.getReadingDistributionGivenShipTuple(shipTuple, sensor)p_reading_given_shipTuple = p_Reading_given_shipTuple.getCount(reading) p *= p_reading_given_shipTuple p_ShipTuple_and_observations.setCount(shipTuple, p) p_ShipTuple_given_observations = normalize(p_ShipTuple_and_observations) return p_ShipTuple_given_observations # END SOLUTION def getReadingDistributionGivenObservations(self, observations, newLocation): # BEGIN SOLUTION oldReadingForNewLocation = self.fetch(newLocation, observations) p_NewReading_and_observations = Counter() p_ShipTuple_given_observations = self.getShipTupleDistributionGivenObservations(observations) for shipTuple, p_shipTuple_given_observations in p_ShipTuple_given_observations.items(): p_Reading_given_shipTuple = self.game.getReadingDistributionGivenShipTuple(shipTuple, newLocation) for reading in Readings.getReadings(): p_reading_given_shipTuple = p_Reading_given_shipTuple.getCount(reading) if oldReadingForNewLocation != None: p_observation_given_ship = 0.0 if oldReadingForNewLocation == reading: p_observation_given_ship = 1.0 p_NewReading_and_observations.incrementCount(reading, p_shipTuple_given_observations * p_reading_given_shipTuple) p_NewReading_given_observations = normalize(p_NewReading_and_observations) return p_NewReading_given_observations # END SOLUTION # BEGIN SOLUTION def fetch(self, key, pairList): for key2, value2 in pairList: if key == key2: return value2 return None # END SOLUTIONclass StaticVPIAgent(StaticBattleshipAgent): """ Computer-controlled battleship agent. This agent plays using value of (perfect) information calculations. The initial implementation is broken, always taking a random bombing action without sensing. You will rewrite it to greedily sense if any sensing action has an expected gain in utility / score (taking into account the cost of sensing). If no sensing action has a greedy gain, then you will select a position tuple to bomb. In this case, your agent should bomb the tuple with the highest expected utility / score according to its current beliefs. """ def getAction(self): # BEGIN SOLUTION self.game.display.pauseGUI() observations = self.observations expectedUtilities = self.getExpectedUtilities(observations)currentBestEU, currentBestBombingOptions = maxes(expectedUtilities) utilityGain = Counter() for location in self.game.getLocations(): if location in observations.keys(): continue expectedNewMEU = 0 p_Reading_given_observations = self.inferenceModule.getReadingDistributionGivenObservations(observations, location) for reading in Readings.getReadings(): outcomeProbability = p_Reading_given_observations.getCount(reading) if outcomeProbability == 0.0: continue newObservations = dict(observations) newObservations[location] = reading outcomeExpectedUtilities = self.getExpectedUtilities(newObservations) outcomeBestEU, outcomeBestActions = maxes(outcomeExpectedUtilities) expectedNewMEU += outcomeBestEU * outcomeProbability utilityGain[location] = expectedNewMEU - currentBestEU - abs(SENSOR_SCORE) bestGain, bestSensorLocations = maxes(utilityGain) if bestGain > 0: return [Actions.makeSensingAction(l) for l in bestSensorLocations] return [Actions.makeBombingAction(o) for o in currentBestBombingOptions] # END SOLUTION # BEGIN SOLUTION def getExpectedUtilities(self, observations): p_ShipTuples = self.inferenceModule.getShipTupleDistributionGivenObservations(observations) expectedUtilities = Counter() for option in self.game.getBombingOptions(): expectedUtility = 0 for ships in p_ShipTuples.keys(): p = p_ShipTuples[ships] utility = BATTLESHIP_SCORE * self.numMatches(option, ships) expectedUtility += p * utility expectedUtilities[option] = expectedUtility return expectedUtilities def numMatches(self, option, ships): matches = 0 for ship in ships: if ship in option: matches += 1 return matches # END
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