What is Heuristic in AI | Why we use Heuristic | How to Calculate Heuristic | Must Watch

Summary

In this video, the concept of heuristic functions in artificial intelligence is explored, explaining their role in making educated guesses to find quick solutions. The script delves into why heuristics are used, particularly in solving complex problems with exponential time complexity, such as in puzzles like the 8 puzzle, and games like chess. It also covers various methods to calculate heuristic values, including Euclidean and Manhattan distances, and the number of misplaced tiles, emphasizing that while heuristics ensure good solutions, they do not guarantee optimal ones. The video is a guide for those interested in AI search techniques, aiming to convert NP problems into more manageable polynomial time solutions.

Takeaways

• 🧠 **Heuristic Definition**: A heuristic in AI is a technique for making educated guesses or finding quick solutions to problems.
• 🔍 **Purpose of Heuristics**: Heuristics are used to simplify the search process in AI by making assumptions to find solutions more efficiently.
• 🔄 **Problem with Uninformed Search**: Uninformed search methods can lead to exponential time complexity, making them impractical for large problems.
• 📈 **Exponential Growth Issue**: The time complexity in problems like the 8-puzzle grows exponentially, leading to a vast search space with billions of possible states.
• 🏹 **Heuristic Advantage**: Heuristics can help in quickly finding good solutions by guiding the search towards more promising paths based on heuristic values.
• 🎯 **Non-Optimality of Heuristics**: While heuristics guarantee good solutions, they do not always ensure the optimal solution due to their nature of making assumptions.
• 🛣️ **Heuristic Methods**: Methods like Euclidean distance, Manhattan distance, and counting misplaced tiles are used to calculate heuristic values.
• 📚 **Euclidean Distance**: The straight-line distance between points, used as a heuristic to estimate the minimum distance to the goal.
• 🚶 **Manhattan Distance**: A heuristic used in grid-based problems like the 8-puzzle, calculating the sum of the horizontal and vertical distances to the goal.
• 🔢 **Misplaced Tiles Count**: A heuristic method that counts the number of tiles not in their goal positions to estimate the distance to the solution.
• 🔧 **Heuristic Use Cases**: Heuristics are particularly useful when a quick solution is needed, and when converting NP problems into more manageable polynomial time complexity.
• 🛠️ **Informed Search Techniques**: Heuristics are a part of informed search techniques, which use additional information, like heuristic values, to guide the search process.

Q & A

What is a heuristic function in artificial intelligence?

A heuristic function in AI is a technique designed to solve a problem quickly by making assumptions and finding a quick solution or a 'rule of thumb' to guide the search towards a good solution, although it may not always be the optimal one.

Why are heuristic values used in artificial intelligence?

Heuristic values are used to reduce the time complexity of solving problems, especially in cases where the search space is exponentially large, by guiding the search towards promising paths and avoiding blind search through all possible states.

What is the difference between informed and uninformed search in AI?

Informed search uses heuristic information to guide the search process, making it more efficient, while uninformed search, also known as blind search, explores all possible states without any guidance, which can be time-consuming and inefficient for large search spaces.

What is the time complexity of an uninformed search in the worst case for an 8 puzzle problem?

The worst-case time complexity for an uninformed search in an 8 puzzle problem is O(b^d), where b is the branching factor and d is the depth of the search space, approximately 3^20 states.

What is the significance of heuristic functions in solving NP problems?

Heuristic functions help in solving NP (non-polynomial) problems by reducing the search time from exponential to polynomial, thus providing quick solutions, although not necessarily the optimal ones.

How does a heuristic function differ from a greedy method?

While both heuristic functions and greedy methods aim to find good solutions quickly, heuristic functions provide a way to estimate the cost to reach the goal state and guide the search, whereas greedy methods simply choose the most promising option at each step without considering the overall path.

What is Euclidean distance and how is it used in calculating heuristic values?

Euclidean distance is the straight-line distance between two points in a plane, calculated using the formula \(\sqrt{(X2 - X1)^2 + (Y2 - Y1)^2}\). It is used in heuristic calculations to estimate the minimum distance to the goal state.

What is Manhattan distance and how does it relate to the 8 puzzle problem?

Manhattan distance is the sum of the absolute differences of their Cartesian coordinates, representing the distance a person would travel on a grid of streets (like in Manhattan). It is used in the 8 puzzle problem to calculate the heuristic value based on the number of moves needed to align tiles vertically or horizontally to their correct positions.

What is the concept of 'misplaced tiles' in the context of the 8 puzzle problem?

In the context of the 8 puzzle problem, 'misplaced tiles' refers to the count of tiles that are not in their correct positions in the goal state. This count is used as a heuristic to estimate how far the current state is from the goal state.

Why might a heuristic not always guarantee an optimal solution?

A heuristic does not always guarantee an optimal solution because it is based on assumptions and estimations that simplify the search process. There may be scenarios or obstacles not accounted for in the heuristic that could lead to a suboptimal path being chosen.

What are some common informed search techniques that use heuristic values?

Some common informed search techniques that use heuristic values include Heuristic DFS, Heuristic BFS, Hill Climbing, and the A* algorithm, which are designed to find good solutions more efficiently than uninformed search methods.