Informed Search in AI: Concepts, Techniques, and Applications

Informed search in AI refers to a class of algorithms that solve problems using heuristics, which are domain-specific knowledge that helps find optimal solutions faster. These techniques reduce unnecessary computation by prioritising the most promising paths toward a goal. You’ll see informed search in action across industries, from GPS navigation and supply chain routing to natural language processing and AI game engines.

Informed search relies on heuristic functions and techniques, such as A* Search and Greedy Best-First Search, which make problem-solving more efficient by combining actual and estimated costs to guide intelligent decisions. In this article, you’ll explore the fundamentals of informed search in AI, heuristic-based methods, and more.

Understanding Informed Search in AI: Core Concepts 

Informed search in AI is a selective search approach that uses additional knowledge to decide which paths in a problem space are worth exploring. Instead of expanding nodes arbitrarily, it evaluates them using a scoring system, typically based on how close they appear to be to the goal. This leads to faster results and lower memory usage, especially in large search spaces where blind exploration is inefficient.

This strategy is effective in use cases where you can estimate progress toward a target, for example, when solving a grid-based puzzle, routing shipments, or planning moves in a decision tree. In such tasks, informed search improves both scalability and responsiveness.

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Before exploring specific algorithms, it's important to grasp the core concepts that define informed search. Let’s understand the core concepts of informed search in AI. 

1. Heuristic Function (h(n)): This function estimates the remaining cost from a given node to the goal. A heuristic is considered admissible if it never overestimates the actual cost. If it also satisfies the triangle inequality between nodes, it is consistent, helping algorithms avoid re-evaluating the same states.
2. Cost Function (f(n) = g(n) + h(n)): In algorithms like A*, this function determines the order in which nodes are evaluated. g(n) tracks the actual cost to reach the current node, and h(n) estimates the price from that node to the goal. This balance allows the search to consider both past effort and future potential.
3. Completeness and Optimality: A search algorithm is complete if it finds a solution when one exists. It is optimal if that solution is the least costly. A* guarantees both properties when the heuristic used is admissible and consistent.

Also Read: Local Search Algorithm in Artificial Intelligence: A Guide

Quick Comparison: Informed vs. Uninformed Search

Here’s how informed search in AI differs from uninformed methods across key performance and decision-making factors.

Below, you will explore each of these components in depth to gain a better understanding of the informed search concepts in AI.

Key Concepts of Informed Search in AI with Applications and Examples

Informed search in AI uses advanced techniques that incorporate domain-specific knowledge, guiding search algorithms toward the most promising paths to find optimal solutions. The core concepts behind informed search play a critical role in enhancing the functionality of AI by enabling more efficient and intelligent decision-making. 

In the sections below, you will explore how each of these components contributes to the overall effectiveness of informed search in solving complex problems.

1. Heuristics function

Heuristic functions, denoted as h(n), estimate the remaining cost from a given node to the goal, enabling informed search algorithms to prioritize promising paths over exploring all options. This improves efficiency by reducing both the number of node expansions and memory usage. 

A heuristic is considered admissible if it never overestimates the actual cost, ensuring optimal solutions in algorithms like A*. If it is also consistent, meaning h(N) ≤ cost(N, P) + h(P), the algorithm avoids reprocessing nodes, which is crucial for performance in graph-based problems.

Common Examples

• Manhattan Distance: Calculates the sum of horizontal and vertical moves in grid maps, both admissible and often exact in such spaces.
• Hamming Distance: Which counts elements out of place (like in sliding-tile puzzles), is easy to compute, but can be less informative.
• Chess Heuristics: Combine piece values and positional factors to estimate board strength and guide deeper search.

Also Read: 17 AI Challenges in 2025: How to Overcome Artificial Intelligence Concerns?

Extended Applications

• Robotics and Navigation: Heuristics, such as Euclidean or Chebyshev distances, help robots evaluate direct paths and avoid unnecessary detours.
• Natural Language Parsing: Estimators assess how far a partial parse is from forming a valid sentence so that the parser can avoid unlikely syntactic paths.
• Planning and Scheduling: In logistics, simplified ("relaxed") versions of scheduling problems yield heuristics that guide resource allocation and task prioritization.

Impact on Performance

When a heuristic is well-constructed:

1. Search Space Reduction: A* explores far fewer nodes. Instead of branching out in every direction, it focuses on promising avenues, which can slash runtime from exponential to manageable levels.
2. Balanced Decision-Making: The combined formula f(n) = g(n) + h(n) balances actual travel cost (g(n)) with predicted remaining cost (h(n)). This enables A* to chart precise and efficient routes.
3. Optimized Memory Use: With a consistent heuristic, once a node is evaluated, it is no longer needed to return to the open set. This avoids duplicated effort and limits memory growth.

Together, these qualities enable informed search in AI to handle problems that are otherwise intractable with blind search methods, making heuristic quality a decisive factor in algorithm success.

2. Cost Function (f(n) = g(n) + h(n))

In informed search algorithms like A*, the cost function f(n) plays a central role in deciding which node to expand next. It combines two values:

• g(n): the cumulative cost from the start node to the current node n
• h(n): the heuristic estimate of the cost from node n to the goal

Together, f(n) = g(n) + h(n) provides an estimate of the total cost of a path that goes through node n. The algorithm always expands the node with the lowest f(n) value first. This approach enables the search to balance what it has already spent (g(n)) with what it expects to spend (h(n)) to reach the goal.

Why This Combination Works

The cost function f(n) = g(n) + h(n) combines two distinct strategies: Uniform-Cost Search, which relies on actual cost g(n), and Greedy Best-First Search, which uses heuristic h(n). Uniform-Cost ensures optimality but is resource-intensive, while Greedy is faster but can overlook better paths. A* integrates both approaches, selecting paths based on cumulative and estimated cost. When h(n) is admissible and consistent, A* guarantees both completeness and optimality.

Also Read: Difference between Informed and Uninformed search

Understanding Its Performance

• A well-designed cost function f(n) improves search efficiency by expanding only the most promising paths, reducing node exploration, and speeding up decisions.
• Accurate heuristics shrink the priority queue, minimizing memory usage by limiting the number of irrelevant nodes considered.
• Scalability also improves when h(n) approximates actual cost, A* performs near the speed of Greedy Best-First Search while retaining optimality. If h(n) is weak, it defaults to the slower behavior of Uniform-Cost Search.

Variants of A Based on the Cost Function*

Several variations of A* adjust how f(n) is calculated or applied to suit different performance needs:

• Iterative Deepening A*: Combines the memory efficiency of depth-first search with A*'s optimality. It uses successive cost thresholds and is ideal for memory-constrained environments.
• Weighted A*: Modifies the cost function to f(n)=g(n)+w×h(n) where w>1. It sacrifices optimality for speed, commonly used when fast, near-optimal paths are acceptable.
• Anytime A*: Starts with a high heuristic weight to quickly find a viable path, then progressively reduces the weight to converge on optimality. This makes it suitable for time-sensitive tasks that benefit from incremental improvements.

Also Read: Top 10 Artificial Intelligence Tools & Frameworks

To fully understand the effectiveness of informed search algorithms, it's essential to explore two key properties: completeness and optimality.

3. Completeness and Optimality

Completeness and optimality are critical properties of search algorithms, especially in the context of informed search in AI.

Completeness: A search algorithm is complete if it guarantees a solution exists when one is reachable. Informed algorithms like A* are complete when the search space is finite, all step costs are non-negative, and the goal is explicitly defined. These conditions ensure the algorithm doesn’t enter infinite loops or skip feasible paths.

Optimality: A search algorithm is complete if it guarantees a solution exists when one is reachable. Informed algorithms, such as A*, are complete when the search space is finite, all step costs are non-negative, and the goal is explicitly defined. These conditions ensure the algorithm doesn’t enter infinite loops or skip feasible paths.

Also Read: 5 Significant Benefits of Artificial Intelligence [Deep Analysis]

Key Conditions

A* balances path cost and heuristic estimates using the evaluation function f(n) = g(n) + h(n), ensuring completeness and optimality when these conditions are satisfied.

A* balances path cost and heuristic estimates using the evaluation function f(n) = g(n) + h(n), ensuring completeness and optimality when these conditions are satisfied.

Before exploring the practical application of informed search in AI, let's first learn about the core techniques, Best-First Search and A*.

Key Informed Search Algorithms and Techniques in AI

Informed search in AI methods utilize domain-specific estimates to explore problem spaces efficiently. By evaluating both past costs and future promises, these algorithms, Best-First Search and A*, guide decisions toward promising solutions while avoiding exhaustive exploration. You’ll discover how each works, when each is most effective, and see practical implementations in Python.

1. Best-First Search

How It Works: Best‑First Search prioritizes nodes based purely on the heuristic value h(n), selecting the next node that appears closest to the goal. It maintains a priority queue and iterates by expanding the lowest-h node until the goal is reached or options are exhausted.

Illustrated Example: Imagine finding the quickest route from home to a grocery store. Best-First chooses the neighboring location with the smallest estimated distance to the store, then repeats the process, always moving toward the location that appears to be the closest.

Python Implementation

import heapq

def best_first(start, goal, h, neighbors):
    open_set = [(h(start), start)]  # Priority queue to store nodes along with their heuristic values
    visited = set()  # Set to keep track of visited nodes

    while open_set:
        _, node = heapq.heappop(open_set)  # Get the node with the smallest heuristic value
        if node == goal:  # Check if we've reached the goal
            return True  # Path found
        visited.add(node)  # Mark the current node as visited
        for nbr in neighbors(node):  # Explore all possible neighbors of the current node
            if nbr in visited: continue  # Skip already visited nodes
            heapq.heappush(open_set, (h(nbr), nbr))  # Add neighbor to the open set with its heuristic value
    return False  # Return False if no path to the goal is found

Output: This modified code now returns the actual path to the goal as a list of nodes.

Code Explanation:

1. Priority Queue (open_set): We use a priority queue implemented with a heap (heapq) to store the nodes. Each node is paired with its heuristic value h(n), which determines its priority. The node with the smallest heuristic value is expanded first.

2. Visited Set: This set keeps track of the nodes we've already explored, ensuring that we don't revisit them and get stuck in a loop.

3. While Loop: The algorithm continues to process nodes until the goal is found or all possible paths have been explored. At each iteration, the node with the smallest heuristic value is selected, and its neighbors are evaluated.

4. Neighbors: The neighbors function provides the list of possible moves or connections from the current node. Each of these neighbors is checked to ensure they haven't been visited yet.

5. Goal Check: If a node matches the goal, the function returns True, indicating that the path has been found.

Pros and Cons

• Pros: Fast and memory-efficient on well-defined heuristics; simple implementation
• Cons: Not guaranteed to find the optimal path; may loop or get stuck in non-ideal routes.

Also Read: Breadth First Search Algorithm: A Complete Guide for 2025

2. A* Search

How It Differs from Best‑First: A* adds the cost so far (g(n)) to the heuristic (h(n)), selecting nodes that minimize f(n) = g(n) + h(n). This balances exploring cost-effective and goal-promising paths  

Illustrated Example: Finding the shortest path on a weighted map, A* evaluates each route by adding the actual distance traveled so far to an estimate of the remaining distance, avoiding detours that Best‑First might take.

Python Implementation of A* Search:

import heapq

def astar(start, goal, h, neighbors, cost):
    open_set = [(h(start), start)]  # Priority queue to store nodes and their heuristic values
    g = {start: 0}  # Dictionary to store the cost to reach each node
    came_from = {}  # Dictionary to track the path

    while open_set:
        _, current = heapq.heappop(open_set)  # Get the node with the lowest f(n) value
        if current == goal:  # Check if we've reached the goal
            return reconstruct_path(came_from, current)  # Reconstruct and return the path

        for nbr in neighbors(current):  # Explore all neighbors of the current node
            tentative = g[current] + cost(current, nbr)  # Calculate tentative g(n) for the neighbor
            if tentative < g.get(nbr, float('inf')):  # If this is a better path, update
                came_from[nbr] = current  # Mark the current node as the predecessor
                g[nbr] = tentative  # Update the cost to reach this neighbor
                heapq.heappush(open_set, (tentative + h(nbr), nbr))  # Add the neighbor to the open set with updated f(n)
    return None  # Return None if no path to goal is found

def reconstruct_path(came_from, current):
    path = [current]
    while current in came_from:
        current = came_from[current]
        path.append(current)
    return path[::-1]  # Reverse the path to get it from start to goal

Output: The function now returns the actual path from the start to the goal.

Code Explanation:

1. Priority Queue (open_set): Similar to Best-First Search, A* uses a priority queue to store nodes. The priority is determined by the function f(n)=g(n)+h(n), where:

• g(n): The cost to reach the node from the start.
• h(n): The heuristic estimate to the goal.

2. Cost Dictionary (g): This dictionary stores the actual cost to reach each node from the start. It is initialized with the start node having a cost of zero. As the algorithm explores new nodes, it updates their costs based on the path taken.

3. Came From Dictionary (came_from): This keeps track of the path from the goal back to the start. It maps each node to its predecessor, helping to reconstruct the final path once the goal is reached.

4. Neighbor Exploration: For each node, we evaluate its neighbors by calculating the tentative cost to reach each neighbor using the function. g(current)+cost(current,nbr). If this cost is lower than the previously recorded cost for the neighbor, we update the cost and add the neighbor to the priority queue.

5. Reconstructing the Path: Once the goal is reached, the reconstruct_path function traces back from the goal to the start using the came_from dictionary and returns the path in the correct order.

Pros and Cons

• Pros: Guarantees optimal path with admissible and consistent heuristics; efficient when heuristics are strong.
• Cons: High memory usage (O(b^d)); performance depends on the quality of the heuristic.

3. Greedy Best-First Search:

Greedy Best-First Search focuses on selecting the node that seems closest to the goal based purely on the heuristic estimate h(n), without considering the cost incurred to reach that node. The algorithm prioritizes exploring paths that appear most promising according to the heuristic, aiming to reach the goal quickly.

Python Implementation of Greedy Best-First Search

import heapq

def greedy_best_first_search(start, goal, h, neighbors):
    open_set = [(h(start), start)]  # Priority queue to store nodes and their heuristic values
    came_from = {}  # Dictionary to track the path

    while open_set:
        _, current = heapq.heappop(open_set)  # Get the node with the lowest heuristic value (h(n))
        if current == goal:  # Check if we've reached the goal
            return reconstruct_path(came_from, current)  # Reconstruct and return the path

        for nbr in neighbors(current):  # Explore all neighbors of the current node
            if nbr not in came_from:  # If the neighbor has not been visited before
                came_from[nbr] = current  # Mark the current node as the predecessor
                heapq.heappush(open_set, (h(nbr), nbr))  # Add the neighbor to the open set based on h(n)

    return None  # Return None if no path to goal is found

def reconstruct_path(came_from, current):
    path = [current]
    while current in came_from:
        current = came_from[current]
        path.append(current)
    return path[::-1]  # Reverse the path to get it from start to goal

Output Explanation: The algorithm returns the shortest path from the start node to the goal node based on the heuristic. It prioritizes nodes according to their heuristic values, selecting the one that seems closest to the goal. If no path is found, it returns None.

Code Explanation:

1. Priority Queue (open_set): Stores nodes prioritized by their heuristic values h(n). The node with the lowest heuristic value is selected for exploration.

2. Came From Dictionary (came_from): Tracks the predecessor of each node to reconstruct the path once the goal is reached.

3. Neighbor Exploration:For each node, the algorithm explores its neighbors. If a neighbor hasn't been visited, it's added to the open set with its heuristic value h(n.

4. Reconstructing the Path: Once the goal is reached, the path is reconstructed from the goal back to the start using the came_from dictionary.

Pros and Cons

Pros:

• Fast in some cases: Efficient when the heuristic is well-designed and the goal is clearly visible.

Cons:

• Not optimal: The path found may not be the shortest, as the algorithm ignores the cost of reaching nodes.
• Relies on heuristic quality: Poor heuristics can lead the search astray or cause inefficiency.

4. A Search with Memory (IDA):

Iterative Deepening A* (IDA*) is a variation of A* Search that combines depth-first search with A*'s heuristic-based approach, using iterative deepening to avoid high memory usage. It repeatedly performs depth-limited searches with an increasing bound based on the function f(n)=g(n)+h(n), where g(n) is the cost to reach the node and h(n) is the heuristic estimate of the cost from the node to the goal. By incrementally increasing the bound, IDA* ensures that it explores nodes at greater depths in each iteration without the high memory requirements of A*.

Python Implementation of IDA* Search

def ida_star(start, goal, h, neighbors, cost):
    bound = h(start)  # Initialize the bound to the heuristic value of the start node
    path = [start]  # Initialize the path with the start node

    while True:
        t = search(path, 0, bound, goal, h, neighbors, cost)  # Perform the search with the current bound
        if t == 'found':  # Goal found
            return path
        if t == float('inf'):  # No solution found
            return None
        bound = t  # Update the bound for the next iteration

def search(path, g, bound, goal, h, neighbors, cost):
    current = path[-1]  # Get the current node
    f = g + h(current)  # Calculate the f(n) value for the current node

    if f > bound:  # If the f(n) value exceeds the bound, return it
        return f
    if current == goal:  # If the current node is the goal, return 'found'
        return 'found'

    min_bound = float('inf')  # Initialize the minimum bound for the next iteration
    for nbr in neighbors(current):  # Explore all neighbors of the current node
        if nbr not in path:  # Avoid cycles
            path.append(nbr)
            t = search(path, g + cost(current, nbr), bound, goal, h, neighbors, cost)  # Recursive call
            if t == 'found':
                return 'found'
            if t < min_bound:
                min_bound = t  # Update the minimum bound

            path.pop()  # Backtrack

    return min_bound  # Return the minimum bound found

Output Explanation: The function will either return the path from the start node to the goal node or None if no path is found. The algorithm performs a series of depth-limited searches, increasing the limit until it finds the goal or determines that no solution exists.

Code Explanation:

1. Bound Initialization (bound): The initial bound is set to the heuristic of the start node. This bound is adjusted in each iteration based on the value of f(n).

2. Recursive Search (search): The search function is called recursively to explore paths, and each call checks whether the current node exceeds the current bound. If it does, the function returns the bound value; if the goal is found, it returns 'found'.

3. Neighbor Exploration: For each node, the algorithm explores its neighbors, ensuring that no cycles are formed by checking if a neighbor has already been visited. If a neighbor leads to a valid path, it is recursively explored.

4. Backtracking: If a path to the goal is not found, the algorithm backtracks by removing the last node added to the path and continuing the search.

Pros and Cons

Pros:

• Low Memory Usage: Unlike A*, IDA* does not store all nodes in memory, making it more memory-efficient for large search spaces.

Cons:

• Slower for Complex Problems: Since IDA* uses iterative deepening, it may repeat searches multiple times, which can be slower than A* for complex problems.

Also Read: 23+ Top Applications of Generative AI Across Different Industries in 2025

Now that you have covered the techniques behind informed search algorithms, let’s explore how these methods are applied in real-world scenarios across various industries.

Practical Applications and Examples of Informed Search in AI 

Informed search algorithms, such as A* and Minimax, enhanced by heuristics, are crucial in industries like gaming, navigation, and robotics. These algorithms enable faster decision-making by intelligently prioritizing paths based on cost estimates. They optimize problem-solving processes, making them highly efficient in complex environments. 

Below are some key applications of these algorithms across different fields:

1. GPS & Navigation Systems 

• How it works: A* search is used in GPS navigation systems to determine the most efficient route, taking into account dynamic data such as traffic and road closures.
• Explanation: The algorithm combines the actual travel cost with a heuristic estimate of remaining distance, ensuring the quickest path to the destination.
• Example: Google Maps and Waze use A* to optimize travel routes in real-time based on live traffic updates.

Also Read: Future Scope of Artificial Intelligence in Various Industries

2. Game AI  

• How it works: Minimax, combined with heuristics, evaluates potential moves in two-player games like chess, aiming to maximize the player’s advantage while minimizing the opponent’s.
• Explanation: The algorithm considers all possible moves and counters, using heuristic functions to rank the desirability of game states and guide decisions.
• Example: Stockfish, a leading chess engine, utilizes Minimax and heuristics to calculate optimal moves and strategies.

3. Natural Language Processing  

• How it works: Informed search algorithms facilitate parsing complex sentences and conducting semantic searches by evaluating linguistic structures and understanding user intent.
• Explanation: Heuristics are used to match user queries with the most relevant documents or answers, improving accuracy in information retrieval.
• Example: Google Search and Bing use semantic search techniques to provide more contextually relevant results based on user queries.

Also Read: A Guide to the Types of AI Algorithms and Their Applications

4. Supply Chain & Robotics

• How it works: In robotics and supply chain management, A* search optimizes routes for delivery and warehouse navigation by considering obstacles and real-time data.
• Explanation: Robots navigate warehouse environments efficiently, while logistics systems plan the best delivery routes to minimize costs and delivery time.
• Example: Amazon Robotics uses A* to optimize paths for Autonomous Mobile Robots (AMRs) in warehouses, improving operational efficiency.

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How Can upGrad Help You Excel in Informed Search in AI?

Informed search in AI algorithms, such as A and Minimax with heuristics, is pivotal in enhancing decision-making processes across various industries.* These algorithms enable systems to evaluate and prioritize paths efficiently, leading to optimized solutions in complex problem spaces. Their application spans from navigation systems to game AI, demonstrating its role in modern technology.

However, mastering these algorithms requires a deep understanding of their principles and applications, which can be challenging without structured learning. Professionals and enthusiasts often struggle to grasp the intricacies of informed search techniques and their real-world implementations. upGrad's specialized programs are designed to bridge this knowledge gap. 

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References:
https://www.cs.wmich.edu/gupta/teaching/cs4310/lectureNotes_cs4310/AI%20powered%20Seach%20Techniques%20Prof%20Mahto%20linkedin.pdf
https://iipseries.org/assets/docupload/rsl2024AF233C7BF02A178.pdf