A star and B star search

README.md

@@ -13,3 +13,6 @@
 | Tabu Search |  ✔️ |
 | Breadth First Search |  ✔️ |
 | Depth First Search |  ✔️ |
+| Greedy Best First Search |  ✔️ |
+| A* Search |  ✔️ |
+| B* Search |  ✔️ |

search/a-star-search.py

@@ -0,0 +1,114 @@
+class Node():
+    """A node class for A* Pathfinding"""
+
+    def __init__(self, parent=None, position=None):
+        self.parent = parent
+        self.position = position
+
+        self.g = 0
+        self.h = 0
+        self.f = 0
+
+    def __eq__(self, other):
+        return self.position == other.position
+
+
+def astar(maze, start, end):
+    """Returns a list of tuples as a path from the given start to the given end in the given maze"""
+
+    # Create start and end node
+    start_node = Node(None, start)
+    start_node.g = start_node.h = start_node.f = 0
+    end_node = Node(None, end)
+    end_node.g = end_node.h = end_node.f = 0
+
+    # Initialize both open and closed list
+    open_list = []
+    closed_list = []
+
+    # Add the start node
+    open_list.append(start_node)
+
+    # Loop until you find the end
+    while len(open_list) > 0:
+
+        # Get the current node
+        current_node = open_list[0]
+        current_index = 0
+        for index, item in enumerate(open_list):
+            if item.f < current_node.f:
+                current_node = item
+                current_index = index
+
+        # Pop current off open list, add to closed list
+        open_list.pop(current_index)
+        closed_list.append(current_node)
+
+        # Found the goal
+        if current_node == end_node:
+            path = []
+            current = current_node
+            while current is not None:
+                path.append(current.position)
+                current = current.parent
+            return path[::-1] # Return reversed path
+
+        # Generate children
+        children = []
+        for new_position in [(0, -1), (0, 1), (-1, 0), (1, 0), (-1, -1), (-1, 1), (1, -1), (1, 1)]: # Adjacent squares
+
+            # Get node position
+            node_position = (current_node.position[0] + new_position[0], current_node.position[1] + new_position[1])
+
+            # Make sure within range
+            if node_position[0] > (len(maze) - 1) or node_position[0] < 0 or node_position[1] > (len(maze[len(maze)-1]) -1) or node_position[1] < 0:
+                continue
+
+            # Make sure walkable terrain
+            if maze[node_position[0]][node_position[1]] != 0:
+                continue
+
+            # Create new node
+            new_node = Node(current_node, node_position)
+
+            # Append
+            children.append(new_node)
+
+        # Loop through children
+        for child in children:
+
+            # Child is on the closed list
+            for closed_child in closed_list:
+                if child == closed_child:
+                    continue
+
+            # Create the f, g, and h values
+            child.g = current_node.g + 1
+            child.h = ((child.position[0] - end_node.position[0]) ** 2) + ((child.position[1] - end_node.position[1]) ** 2)
+            child.f = child.g + child.h
+
+            # Child is already in the open list
+            for open_node in open_list:
+                if child == open_node and child.g > open_node.g:
+                    continue
+
+            # Add the child to the open list
+            open_list.append(child)
+
+
+maze = [[0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
+
+start = (0, 0)
+end = (7, 6)
+
+path = astar(maze, start, end)
+print(path)

search/b-star-search..py

@@ -0,0 +1,67 @@
+"""
+Implementing the B* (B-star) search algorithm can be a bit more complex than A* 
+because it requires maintaining both primary and secondary heuristics, as well as balancing the two heuristics during the search. 
+Below is a Python program for a simplified version of B* search. 
+Keep in mind that the B* algorithm can be more complex in practice and often requires careful consideration of heuristics. 
+This example focuses on the concept, but real-world applications may involve additional details and optimizations.
+"""
+import heapq
+
+def b_star_search(graph, start, goal, primary_heuristic, secondary_heuristic):
+    open_set = [(0, start)]  # Priority queue to store (f-cost, state) pairs
+    g_costs = {node: float('inf') for node in graph}  # Initialize g-costs to infinity
+    g_costs[start] = 0
+    visited = set()
+
+    while open_set:
+        f_cost, current_state = heapq.heappop(open_set)
+
+        if current_state == goal:
+            print("Goal found!")
+            return
+
+        if current_state in visited:
+            continue
+
+        visited.add(current_state)
+
+        for neighbor, cost in graph[current_state]:
+            tentative_g_cost = g_costs[current_state] + cost
+            if tentative_g_cost < g_costs[neighbor]:
+                g_costs[neighbor] = tentative_g_cost
+                h_cost = primary_heuristic[neighbor]
+                h2_cost = secondary_heuristic[neighbor]
+                f_cost = tentative_g_cost + max(h_cost, h2_cost)  # B* balance
+                heapq.heappush(open_set, (f_cost, neighbor))
+
+    print("Goal not found!")
+
+# Example usage
+graph = {
+    'A': [('B', 2), ('C', 4)],
+    'B': [('D', 5)],
+    'C': [('D', 7)],
+    'D': [('E', 3)],
+    'E': [],
+}
+
+primary_heuristic = {
+    'A': 8,  # Primary heuristic values for each state, estimated distance to the goal
+    'B': 6,
+    'C': 5,
+    'D': 3,
+    'E': 0,
+}
+
+secondary_heuristic = {
+    'A': 6,  # Secondary heuristic values for each state
+    'B': 4,
+    'C': 3,
+    'D': 1,
+    'E': 0,
+}
+
+start_state = 'A'
+goal_state = 'E'
+
+b_star_search(graph, start_state, goal_state, primary_heuristic, secondary_heuristic)