Informed BFS

from queue import PriorityQueue  

graph = { 

'A': [('B', 3), ('C', 6), ('D', 5)], 

'B': [('E', 9), ('F', 8)], 

'C': [('G', 12), ('H', 14)], 

'D': [('I', 7)], 

'E': [('G', 10)], 

'F': [], 

'G': [], 

'H': [], 

'I': [('J', 1), ('K', 10), ('L', 2)], 

'J': [], 

'K': [], 

'L': [] 

} 

heuristic = { 

'A': 10, 

'B': 8, 

'C': 8, 

'D': 6, 

'E': 5, 

'F': 6, 

'G': 0, 

'H': 0, 

'I': 4, 

'J': 1, 

'K': 10, 

'L': 2 

} 

def best_first_search(graph, start, goal, heuristic): 

visited = set() 

pq = PriorityQueue() 

pq.put((heuristic[start], start)) 

while not pq.empty(): 

_, current_node = pq.get() 

if current_node == goal: 

print("Goal reached:", current_node) 

return 

if current_node not in visited: 

print("Visiting:", current_node) 

visited.add(current_node) 

neighbors = graph[current_node] 

for neighbor, cost in neighbors: 

if neighbor not in visited: 

pq.put((heuristic[neighbor], neighbor)) 

print("Goal not reached") 

start_node = 'A' 

goal_node = 'G' 

best_first_search(graph, start_node, goal_node, heuristic)