Implement search procedures cleanly.

p1_search/search.py

@@ -74,7 +74,7 @@ def tinyMazeSearch(problem):
     return [s, s, w, s, w, w, s, w]
 
 
-def genericSearch(problem, costFunction):
+def genericSearch(problem, getNewCostAndPriority):
     fringe = util.PriorityQueue()
     startState = problem.getStartState()
     fringe.push((startState, [], 0), 0)
@@ -86,11 +86,11 @@ def genericSearch(problem, costFunction):
             return actions
         visited[state] = cost
         for successor, action, stepCost in problem.getSuccessors(state):
-            newCost = costFunction(cost, stepCost)
-            if successor in visited and abs(visited[successor]) <= abs(newCost):
+            newCost, priority = getNewCostAndPriority(cost, stepCost, successor)
+            if successor in visited and visited[successor] <= newCost:
                 continue
             newActions = list(actions) + [action]
-            fringe.push((successor, newActions, newCost), newCost)
+            fringe.push((successor, newActions, newCost), priority)
     print("No path found.")
     raise Exception()
 
@@ -102,24 +102,26 @@ def depthFirstSearch(problem):
     Your search algorithm needs to return a list of actions that reaches the
     goal. Make sure to implement a graph search algorithm.
     """
-    def costFunction(currentCost, stepCost):
-        return currentCost - 1
-    return genericSearch(problem, costFunction)
+    def getNewCostAndPriority(cost, stepCost, successor):
+        newCost = cost + 1
+        return newCost, -newCost
+    return genericSearch(problem, getNewCostAndPriority)
 
 
 def breadthFirstSearch(problem):
     """Search the shallowest nodes in the search tree first."""
-    def costFunction(currentCost, stepCost):
-        return currentCost + 1
-    return genericSearch(problem, costFunction)
+    def getNewCostAndPriority(cost, stepCost, successor):
+        newCost = cost + 1
+        return newCost, newCost
+    return genericSearch(problem, getNewCostAndPriority)
 
 
 def uniformCostSearch(problem):
     """Search the node of least total cost first."""
-    "*** YOUR CODE HERE ***"
-    def costFunction(currentCost, stepCost):
-        return currentCost + stepCost
-    return genericSearch(problem, costFunction)
+    def getNewCostAndPriority(cost, stepCost, successor):
+        newCost = cost + stepCost
+        return newCost, newCost
+    return genericSearch(problem, getNewCostAndPriority)
 
 
 def nullHeuristic(state, problem=None):
@@ -133,9 +135,11 @@ def nullHeuristic(state, problem=None):
 def aStarSearch(problem, heuristic=nullHeuristic):
     """Search the node that has the lowest combined cost and heuristic first."""
     "*** YOUR CODE HERE ***"
-    def costFunction(currentCost, stepCost):
-        return currentCost + 1
-    return genericSearch(problem, costFunction)
+    def getNewCostAndPriority(cost, stepCost, successor):
+        newCost = cost + stepCost
+        newPriority = newCost + heuristic(successor, problem)
+        return newCost, newPriority
+    return genericSearch(problem, getNewCostAndPriority)
 
 
 # Abbreviations