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50 changes: 15 additions & 35 deletions code/pacman/search/search.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -72,28 +72,6 @@ def tinyMazeSearch(problem):
w = Directions.WEST
return [s, s, w, s, w, w, s, w]


def nullHeuristic(state, problem=None):
"""
A heuristic function estimates the cost from the current state to the nearest
goal in the provided SearchProblem. This heuristic is trivial.
"""
return 0

def generalPurposeSearch(problem, stack, heuristic=nullHeuristic):
marked = set()
stack.push(([], problem.getStartState(), 0, 0))
while not stack.isEmpty():
(directions, nextState, costSoFar, stateValue) = stack.pop()
if problem.isGoalState(nextState):
return directions
if nextState not in marked:
successors = problem.getSuccessors(nextState)
for (successor, direction, cost) in successors:
hCost = heuristic(successor, problem)
stack.push((directions + [direction], successor, costSoFar + cost, costSoFar + cost + hCost))
marked.add(nextState)

def depthFirstSearch(problem):
"""
Search the deepest nodes in the search tree first.
Expand All @@ -108,28 +86,30 @@ def depthFirstSearch(problem):
print "Is the start a goal?", problem.isGoalState(problem.getStartState())
print "Start's successors:", problem.getSuccessors(problem.getStartState())
"""
# Problem with this code: does not use linear space
return generalPurposeSearch(problem, util.Stack())

"*** YOUR CODE HERE ***"
util.raiseNotDefined()

def breadthFirstSearch(problem):
"""Search the shallowest nodes in the search tree first."""
return generalPurposeSearch(problem, util.Queue())

"*** YOUR CODE HERE ***"
util.raiseNotDefined()

def uniformCostSearch(problem):
"""Search the node of least total cost first."""
return generalPurposeSearch(
problem,
util.PriorityQueueWithFunction(lambda x: x[2]))

"*** YOUR CODE HERE ***"
util.raiseNotDefined()

def nullHeuristic(state, problem=None):
"""
A heuristic function estimates the cost from the current state to the nearest
goal in the provided SearchProblem. This heuristic is trivial.
"""
return 0

def aStarSearch(problem, heuristic=nullHeuristic):
"""Search the node that has the lowest combined cost and heuristic first."""
return generalPurposeSearch(
problem,
util.PriorityQueueWithFunction(lambda x: x[3]),
heuristic)
"*** YOUR CODE HERE ***"
util.raiseNotDefined()


# Abbreviations
Expand Down
105 changes: 21 additions & 84 deletions code/pacman/search/searchAgents.py
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Original file line number Diff line number Diff line change
Expand Up @@ -294,14 +294,15 @@ def getStartState(self):
Returns the start state (in your state space, not the full Pacman state
space)
"""
visitedCorners = tuple([x == self.startingPosition for x in self.corners])
return (self.startingPosition, visitedCorners)
"*** YOUR CODE HERE ***"
util.raiseNotDefined()

def isGoalState(self, state):
"""
Returns whether this search state is a goal state of the problem.
"""
return state[1] == (True, True, True, True)
"*** YOUR CODE HERE ***"
util.raiseNotDefined()

def getSuccessors(self, state):
"""
Expand All @@ -315,18 +316,15 @@ def getSuccessors(self, state):
"""

successors = []
(currentPosition, goals) = state
for action in [Directions.NORTH, Directions.SOUTH, Directions.EAST, Directions.WEST]:
# Add a successor state to the successor list if the action is legal
# Here's a code snippet for figuring out whether a new position hits a wall:
x,y = currentPosition
dx, dy = Actions.directionToVector(action)
nextx, nexty = int(x + dx), int(y + dy)
hitsWall = self.walls[nextx][nexty]
if not hitsWall:
revisedGoals = tuple([goals[i] or (nextx, nexty) == corner
for (i, corner) in enumerate(self.corners)])
successors.append((((nextx, nexty), revisedGoals), action, 1))
# x,y = currentPosition
# dx, dy = Actions.directionToVector(action)
# nextx, nexty = int(x + dx), int(y + dy)
# hitsWall = self.walls[nextx][nexty]

"*** YOUR CODE HERE ***"

self._expanded += 1 # DO NOT CHANGE
return successors
Expand Down Expand Up @@ -358,29 +356,11 @@ def cornersHeuristic(state, problem):
shortest path from the state to a goal of the problem; i.e. it should be
admissible (as well as consistent).
"""
def manhattanDist(xy1, xy2):
return abs(xy1[0] - xy2[0]) + abs(xy1[1] - xy2[1])

def manhattanHeuristicRecursive(position, extraPositions):
"The Manhattan distance heuristic for a CornerSearchProblem"
if len(extraPositions) == 0:
return 0
distances = [(manhattanDist(position, extraPosition), extraPosition) for extraPosition in extraPositions]
sortedDistances = sorted(distances)
if len(distances) > 1:
extra = manhattanHeuristicRecursive(sortedDistances[0][1], [pos for (i, pos) in sortedDistances[1:]])
else:
extra = 0
return sortedDistances[0][0] + extra

corners = problem.corners # These are the corner coordinates
walls = problem.walls # These are the walls of the maze, as a Grid (game.py)
if problem.isGoalState(state):
return 0
(currentPosition, visitedCorners) = state
activeCorners = [corner for (i, corner) in enumerate(corners) if not visitedCorners[i]]
heuristic = manhattanHeuristicRecursive(currentPosition, activeCorners)
return heuristic

"*** YOUR CODE HERE ***"
return 0 # Default to trivial solution

class AStarCornersAgent(SearchAgent):
"A SearchAgent for FoodSearchProblem using A* and your foodHeuristic"
Expand Down Expand Up @@ -473,55 +453,8 @@ def foodHeuristic(state, problem):
problem.heuristicInfo['wallCount']
"""
position, foodGrid = state
heuristics = FoodHeuristics()
heuristic = heuristics.mstHeuristic([position] + foodGrid.asList())
return heuristic


"""
The following are three food heuristics that obtain scores of 2/4,
3/4, and 4/4.
"""
class FoodHeuristics:

def euclideanDist(self, xy1, xy2):
return ( (xy1[0] - xy2[0]) ** 2 + (xy1[1] - xy2[1]) ** 2 ) ** 0.5

def simpleFoodHeuristic(self, state, problem):
"""
Just checks how much food is left.

Gets 2/4.
"""
position, foodGrid = state
return len(foodGrid.asList())

def minimalDistHeuristic(self, position, extraPositions):
"""
Gets 3/4
"""
def findMinimalDist(position, extraPositions):
dists = [self.euclideanDist(position, pos2) for pos2 in extraPositions]
return min(dists)
minimalDists = [findMinimalDist(pos, list(set(extraPositions) - set([pos])) + [position]) for pos in extraPositions]
return sum(sorted(minimalDists))

def mstHeuristic(self, dots):
"""
Gets 4/4
"""
from scipy.sparse import csr_matrix
from scipy.sparse.csgraph import minimum_spanning_tree
pairs = [(i, j) for i in range(len(dots)) for j in range(len(dots)) if i < j]
row = [dot1 for (dot1, dot2) in pairs]
col = [dot2 for (dot1, dot2) in pairs]
data = [self.euclideanDist(dots[dot1], dots[dot2]) for (dot1, dot2) in pairs]
matrix = csr_matrix((data, (row, col)), shape=(len(dots), len(dots)))
Tcsr = minimum_spanning_tree(matrix)
return Tcsr.sum()



"*** YOUR CODE HERE ***"
return 0

class ClosestDotSearchAgent(SearchAgent):
"Search for all food using a sequence of searches"
Expand Down Expand Up @@ -550,7 +483,9 @@ def findPathToClosestDot(self, gameState):
food = gameState.getFood()
walls = gameState.getWalls()
problem = AnyFoodSearchProblem(gameState)
return search.bfs(problem)

"*** YOUR CODE HERE ***"
util.raiseNotDefined()

class AnyFoodSearchProblem(PositionSearchProblem):
"""
Expand Down Expand Up @@ -584,7 +519,9 @@ def isGoalState(self, state):
complete the problem definition.
"""
x,y = state
return state in self.food.asList()

"*** YOUR CODE HERE ***"
util.raiseNotDefined()

def mazeDistance(point1, point2, gameState):
"""
Expand Down