Informed Search Algorithms in AI (2025 Guide): A* vs Greedy Best-First Search with Use Cases

Artificial Intelligence is all about making machines think smart—and a huge part of that is figuring out how they search for solutions. When the search space is large and complex, informed search algorithms step in as the intelligent solution.

In this 2025 guide, we’ll dive into:

• What informed search algorithms are
• The difference between A* and Greedy Best-First Search
• Heuristic functions explained
• Real-world applications of both
• Code snippets and performance tips
• Learning resources to go deeper

🚀 What Are Informed Search Algorithms?

Informed search (also called heuristic search) uses domain-specific knowledge to make better decisions during traversal. Unlike uninformed search (like BFS or DFS), these algorithms know which direction is more promising.

🤖 They rely on heuristic functions that estimate the cost to reach the goal from any given node.

🔍 A* Search Algorithm

🔧 Formula:

f(n) = g(n) + h(n)

• g(n): Cost from the start node to node n
• h(n): Estimated cost from node n to goal (heuristic)
• f(n): Total estimated cost of the cheapest solution through n

✅ Characteristics:

• Finds optimal paths if the heuristic is admissible (never overestimates)
• Uses both actual cost and estimated cost

⚡ Greedy Best-First Search

🔧 Formula:

f(n) = h(n)

• Only focuses on the heuristic estimate (ignores actual cost so far)
• Tries to reach the goal as quickly as possible, often taking shortcuts

⚠️ Trade-offs:

• Faster than A* in some cases
• Not guaranteed to find the shortest path

🧭 Visual Example

Let's say you're navigating a maze.

• A* looks at how far you’ve come and how far you need to go.
• Greedy just sees the nearest exit and rushes that way — sometimes hitting dead ends.

💻 Python Implementation

Here’s a simple way to illustrate the logic using the networkx library:

import networkx as nx
import heapq

def heuristic(a, b):
    # Simple Euclidean distance
    return ((a[0]-b[0])**2 + (a[1]-b[1])**2)**0.5

def a_star_search(graph, start, goal):
    frontier = []
    heapq.heappush(frontier, (0, start))
    came_from = {start: None}
    cost_so_far = {start: 0}

    while frontier:
        _, current = heapq.heappop(frontier)
        if current == goal:
            break
        for next in graph.neighbors(current):
            new_cost = cost_so_far[current] + graph[current][next]['weight']
            if next not in cost_so_far or new_cost < cost_so_far[next]:
                cost_so_far[next] = new_cost
                priority = new_cost + heuristic(goal, next)
                heapq.heappush(frontier, (priority, next))
                came_from[next] = current

    return came_from

This code can be extended for Greedy Best-First by using only the heuristic value for priority.

🏗️ Real-World Use Cases in 2025

📈 Performance and Trade-offs

📚 Want to Learn More?

To truly master the logic behind these algorithms and apply them to real-world problems, check out the Applied AI Course blog. Their tutorials cover:

• Heuristics in-depth
• Decision-making algorithms
• Graph search problems with examples
• Interview-level explanations for AI aspirants

🧑‍🏫 Whether you're preparing for a tech interview or building your next AI project, Applied AI’s content helps bridge the gap between theory and practice.

🧠 Final Thoughts

Informed search algorithms are what make AI seem intelligent. Whether you're optimizing robot movement or enhancing gaming AI, choosing the right algorithm — A* for optimal results or Greedy for faster execution — depends on your goal.

By combining clear heuristics with the right strategy, you can build AI that not only thinks but thinks smart.