Create puzzle.py

Author: greenratextra1-create

puzzle.py

@@ -0,0 +1,205 @@
+# Q2: Robot Evacuation Planning using Best First Search
+
+# Grid dimensions: 10 rows, 20 columns
+# 0 = Walkable (Hallway/Room interior)
+# 1 = Wall/Blocked
+
+# Approximated Floor Plan based on image
+# Row 4 is the main hallway
+# Entry at (8, 4), Exit at (4, 18)
+
+grid = [
+    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], # 0: Top Wall
+    [1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1], # 1: Rooms
+    [1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 1], # 2: Rooms
+    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1], # 3: Wall separating rooms from hall
+    [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], # 4: Main Hallway (Exit at end)
+    [1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1], # 5: Structures below hall
+    [1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1], # 6: More structures
+    [1, 1, 1, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1], # 7: Walls
+    [1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], # 8: Entry area (at 8,4)
+    [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]  # 9: Bottom Wall
+]
+
+rows = 10
+cols = 20
+
+# Entry and Exit
+START = (8, 4)  # Entry (Row 8, Col 4)
+GOAL = (4, 18)  # Exit (Row 4, Col 18)
+
+class Node:
+    def __init__(self, state, parent=None, action=None, path_cost=0):
+        self.state = state  # (row, col)
+        self.parent = parent
+        self.action = action # "Up", "Down", "Left", "Right"
+        self.path_cost = path_cost
+
+class PriorityQueue:
+    def __init__(self, f):
+        self.data = []
+        self.f = f
+
+    def add(self, node):
+        self.data.append(node)
+        self.data.sort(key=self.f)
+
+    def pop(self):
+        return self.data.pop(0)
+
+    def top(self):
+        return self.data[0]
+
+    def is_empty(self):
+        return len(self.data) == 0
+
+class Problem:
+    def __init__(self, initial, goal, grid):
+        self.initial = initial
+        self.goal = goal
+        self.grid = grid
+
+    def is_goal(self, state):
+        return state == self.goal
+
+    def ACTIONS(self, state):
+        r, c = state
+        actions = []
+        # Down, Up, Right, Left
+        # Using simple step cost 1 for all moves
+        moves = [
+            ("Down",  (1, 0)),
+            ("Up",    (-1, 0)),
+            ("Right", (0, 1)),
+            ("Left",  (0, -1))
+        ]
+        
+        for name, (dr, dc) in moves:
+            nr, nc = r + dr, c + dc
+            # Check boundaries and walls
+            if 0 <= nr < rows and 0 <= nc < cols:
+                if self.grid[nr][nc] == 0: # 0 is walkable
+                    actions.append(name)
+        return actions
+
+    def RESULT(self, state, action):
+        r, c = state
+        if action == "Down":  return (r + 1, c)
+        if action == "Up":    return (r - 1, c)
+        if action == "Right": return (r, c + 1)
+        if action == "Left":  return (r, c - 1)
+        return state
+
+    def ACTION_COST(self, s, action, s_prime):
+        return 1 # Uniform cost for grid movement
+
+# Heuristic: Manhattan Distance
+def heuristic(node):
+    r1, c1 = node.state
+    r2, c2 = GOAL
+    # h(n) = |x1 - x2| + |y1 - y2|
+    return abs(r1 - r2) + abs(c1 - c2)
+
+# Evaluation function f(n) = g(n) for Uniform Cost Search (UCS)
+def f(node):
+    # Justification: UCS uses path cost g(n) to find the optimal path.
+    return node.path_cost
+
+def EXPAND(problem, node):
+    s = node.state
+    for action in problem.ACTIONS(s):
+        s_prime = problem.RESULT(s, action)
+        cost = node.path_cost + problem.ACTION_COST(s, action, s_prime)
+        yield Node(state=s_prime, parent=node, action=action, path_cost=cost)
+
+def BEST_FIRST_SEARCH(problem, f):
+    node = Node(problem.initial)
+    frontier = PriorityQueue(f)
+    frontier.add(node)
+    
+    # 2D reached table
+    reached = [[None for _ in range(cols)] for _ in range(rows)]
+    reached[node.state[0]][node.state[1]] = node
+    
+    explored = 0
+    
+    while not frontier.is_empty():
+        node = frontier.pop()
+        explored += 1
+        
+        if problem.is_goal(node.state):
+            return node, explored
+        
+        for child in EXPAND(problem, node):
+            r, c = child.state
+            if reached[r][c] is None or child.path_cost < reached[r][c].path_cost:
+                reached[r][c] = child
+                frontier.add(child)
+                
+    return None, explored
+
+def get_path(node):
+    path = []
+    while node:
+        path.append(node.state)
+        node = node.parent
+    return path[::-1]
+
+def print_grid_with_path(grid, path):
+    print("\nEvaluated Path on Grid:")
+    path_set = set(path)
+    
+    # Header
+    print("   ", end="")
+    for c in range(cols): print(f"{c%10}", end=" ")
+    print()
+    
+    for r in range(rows):
+        print(f"{r:<2} ", end="")
+        for c in range(cols):
+            if (r, c) == START:
+                print("S", end=" ")
+            elif (r, c) == GOAL:
+                print("E", end=" ")
+            elif (r, c) in path_set:
+                print(".", end=" ") # Path marker
+            elif grid[r][c] == 1:
+                print("#", end=" ") # Wall
+            else:
+                print(" ", end=" ") # Empty space
+        print()
+
+if __name__ == "__main__":
+    problem = Problem(START, GOAL, grid)
+    
+    print("Starting Uniform Cost Search (UCS)...")
+    print(f"Start: {START}, Goal: {GOAL}")
+    
+    solution, explored = BEST_FIRST_SEARCH(problem, f)
+    
+    if solution:
+        path = get_path(solution)
+        print("\nGoal Found!")
+        print(f"Path Length: {len(path)}")
+        print(f"Total Cost: {solution.path_cost}")
+        print(f"Nodes Explored: {explored}")
+        
+        print_grid_with_path(grid, path)
+        
+        print("\nPath Steps:")
+        curr = solution
+        steps = []
+        while curr.parent:
+            steps.append(f"{curr.action} -> {curr.state}")
+            curr = curr.parent
+        for s in reversed(steps):
+            print(s)
+            
+        print("\nEvaluation Cost Function Justification:")
+        print("Function: Path Cost f(n) = g(n)")
+        print("Reason: Uniform Cost Search expands nodes based on total path cost from the start.")
+        print("This guarantees shortest path finding in a grid with uniform step costs,")
+        print("exploring in 'waves' rather than just aiming for the goal.")
+        
+    else:
+        print("No path found!")