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The Pros and Cons of Admissible Heuristic in Search Algorithms

Admissible Heuristic: A Tool for Efficient Problem Solving

As humans, we are constantly solving problems. From simple daily tasks like getting dressed in the morning to complex problems like developing a new product, every problem we encounter requires some level of problem-solving skills. But how do we ensure that we are solving problems efficiently and effectively? One way to approach this is by using an admissible heuristic.

Admissible heuristic is a technique used in problem-solving algorithms that provides a quick estimate of the distance to the goal. In simple terms, heuristics are rules of thumb that allow us to make educated guesses about the best possible solution without examining every possible option. Admissible heuristic, on the other hand, is a specific type of heuristic that guarantees the solution found will never be worse than the actual solution.

To understand admissible heuristic more clearly, it is essential to delve deeper into what goes on behind the algorithm.

What is a Heuristic?

Heuristics are methods used to solve problems by taking a shortcut or using broad generalizations in estimation. In other words, it is a problem-solving strategy that helps find a solution to a problem by using a rule that is not perfect or guaranteed to arrive at the solution.

For example, suppose you were asked to find the shortest route to a destination in a new city. You would likely examine a map of the city and look for the shortest route between your location and the destination. But if you were to do this without knowing much about the city, you might not consider alternate routes or street closures. To solve the problem more efficiently, you might use a heuristic like always turning right at intersections. Although it may not be the shortest path, it is a quick and easy method that will likely give you an acceptable solution.

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Heuristics are, therefore, practical or efficient solutions to problems by solving them faster and with less computational power.

What is Admissible Heuristic?

An admissible heuristic is similar to a regular heuristic, but it comes with an important guarantee. Admissible heuristic guarantees that the estimated cost to reach the goal is never higher than its actual cost. This is a powerful guarantee that ensures the algorithm will never find a suboptimal solution.

Let’s go back to the example of finding the shortest path in a new city. Now, suppose you not only want the shortest route but the quickest route. In this case, you could use a heuristic like “always take the freeway.” This heuristic would likely give you a quick and easy solution, but it is not guaranteed to give you the shortest route. To make it an admissible heuristic, you could add the condition that the freeway must be at least as short as the alternate route.

Admissible heuristic is also known as an optimistic heuristic because it assumes that reaching the goal will be easier than it actually is. This may seem counterintuitive, but it helps the algorithm to avoid wasting time on solutions that are unlikely to work. By underestimating the actual cost to reach the goal, admissible heuristics guide the algorithm towards more promising paths that lead to an optimal solution.

Real-Life Examples

Admissible heuristic has numerous practical applications, from video game programming to computer networking. Let’s take a look at some real-life examples of admissible heuristic in action.

1. The Rubik’s Cube

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Solving a Rubik’s Cube is a classic example of problem-solving using admissible heuristic. To solve a Rubik’s Cube, you need to find a sequence of moves that will rearrange all the colored squares on each face, so that each of the six faces has one solid color.

One way to approach the problem is by finding all possible sequences of moves and examining each one to see which one works. However, this method is inefficient, and it may take a long time to find a solution.

To solve the Rubik’s Cube more efficiently, you could use an admissible heuristic called the Manhattan distance. The Manhattan distance is a technique that involves calculating the number of moves needed to move a block from its current location to the goal location. By using this heuristic, the algorithm guides itself towards the goal by choosing moves that will take it closer to the solution.

2. GPS Navigation

GPS navigation systems use admissible heuristic to provide the shortest or quickest route to the destination. GPS navigation provides different routes to the destination with various expected durations. Through admissible heuristic, it provides the optimal route that avoids unnecessary and time-consuming traffic and roads.

3. Machine Learning

Machine learning and artificial intelligence involve using algorithms to learn from data and provide predictions or decisions based on that data. Admissible heuristic in machine learning guides the algorithm towards more promising paths, leading to accurate predictions.


Admissible heuristics are efficient tools for problem-solving, and their importance is on the rise in today’s world full of data complexities. They are practical, efficient, and provide guaranteed results. Admissible heuristics are prevalent in different areas such as gaming, navigation, machine learning, and many others that require a fast, efficient solution to complex problems. Understanding admissible heuristic enables individuals and organizations to make data-driven decisions that provide a competitive edge.


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