From de6278779071e687b225a875e575bfe00ea0f00c Mon Sep 17 00:00:00 2001
From: Felix Martin <mail@felixm.de>
Date: Tue, 19 Oct 2021 14:23:29 -0400
Subject: [PATCH] Start to implement search procedures.

---
 p1_search/search.py | 57 ++++++++++++++++++++++++++++++++-------------
 1 file changed, 41 insertions(+), 16 deletions(-)

diff --git a/p1_search/search.py b/p1_search/search.py
index 7d79009..207b0e1 100644
--- a/p1_search/search.py
+++ b/p1_search/search.py
@@ -4,7 +4,7 @@
 # educational purposes provided that (1) you do not distribute or publish
 # solutions, (2) you retain this notice, and (3) you provide clear
 # attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
-# 
+#
 # Attribution Information: The Pacman AI projects were developed at UC Berkeley.
 # The core projects and autograders were primarily created by John DeNero
 # (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
@@ -19,6 +19,7 @@ Pacman agents (in searchAgents.py).
 
 import util
 
+
 class SearchProblem:
     """
     This class outlines the structure of a search problem, but doesn't implement
@@ -70,7 +71,29 @@ def tinyMazeSearch(problem):
     from game import Directions
     s = Directions.SOUTH
     w = Directions.WEST
-    return  [s, s, w, s, w, w, s, w]
+    return [s, s, w, s, w, w, s, w]
+
+
+def genericSearch(problem, costFunction):
+    fringe = util.PriorityQueue()
+    startState = problem.getStartState()
+    fringe.push((startState, [], 0), 0)
+    visited = {}
+
+    while not fringe.isEmpty():
+        state, actions, cost = fringe.pop()
+        if problem.isGoalState(state):
+            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):
+                continue
+            newActions = list(actions) + [action]
+            fringe.push((successor, newActions, newCost), newCost)
+    print("No path found.")
+    raise Exception()
+
 
 def depthFirstSearch(problem):
     """
@@ -78,27 +101,26 @@ 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.
-
-    To get started, you might want to try some of these simple commands to
-    understand the search problem that is being passed in:
-
-    print "Start:", problem.getStartState()
-    print "Is the start a goal?", problem.isGoalState(problem.getStartState())
-    print "Start's successors:", problem.getSuccessors(problem.getStartState())
     """
-    "*** YOUR CODE HERE ***"
-    print "Start:", problem.getStartState()
-    util.raiseNotDefined()
+    def costFunction(currentCost, stepCost):
+        return currentCost - 1
+    return genericSearch(problem, costFunction)
+
 
 def breadthFirstSearch(problem):
     """Search the shallowest nodes in the search tree first."""
-    "*** YOUR CODE HERE ***"
-    util.raiseNotDefined()
+    def costFunction(currentCost, stepCost):
+        return currentCost + 1
+    return genericSearch(problem, costFunction)
+
 
 def uniformCostSearch(problem):
     """Search the node of least total cost first."""
     "*** YOUR CODE HERE ***"
-    util.raiseNotDefined()
+    def costFunction(currentCost, stepCost):
+        return currentCost + stepCost
+    return genericSearch(problem, costFunction)
+
 
 def nullHeuristic(state, problem=None):
     """
@@ -107,10 +129,13 @@ def nullHeuristic(state, problem=None):
     """
     return 0
 
+
 def aStarSearch(problem, heuristic=nullHeuristic):
     """Search the node that has the lowest combined cost and heuristic first."""
     "*** YOUR CODE HERE ***"
-    util.raiseNotDefined()
+    def costFunction(currentCost, stepCost):
+        return currentCost + 1
+    return genericSearch(problem, costFunction)
 
 
 # Abbreviations