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

Summary of the Video "What is Heuristic in AI | Why we use Heuristic | How to Calculate Heuristic"

Main Ideas and Concepts:

Definition of Heuristic:
Heuristic refers to a technique used to make assumptions or educated guesses to find quick solutions to problems, particularly in Artificial Intelligence (AI). It is likened to solving mathematical problems where assumptions simplify the process.
Importance of Heuristic in AI:
Heuristics are employed to tackle complex problems that would otherwise require excessive time and computational resources. They help reduce the time complexity associated with problems, particularly in search algorithms.
Types of Search:
- Uninformed Search: A blind search that explores all possible states without guidance, leading to exponential growth in search space (e.g., O(b^d)).
- Informed Search: Utilizes Heuristic values to guide the search, significantly reducing the number of states explored.
Heuristic Calculation Methods:
- Euclidean Distance: Measures the straight-line distance between two points in a space, calculated using the formula:
  Distance = √((X2 - X1)² + (Y2 - Y1)²)
- Manhattan Distance: Calculates the distance based on vertical and horizontal movements, useful in grid-based problems like the 8-puzzle.
- Number of Misplaced Tiles: Counts how many tiles are out of place in a puzzle, providing a Heuristic value based on the number of moves needed to reach the goal state.
Limitations of Heuristic:
While heuristics guarantee a good solution, they do not guarantee an optimal solution. Blind searches may yield optimal solutions, while Heuristic methods may not.
Applications of Heuristic:
Heuristic methods are particularly useful in converting non-polynomial (NP) problems into polynomial time solutions, making them more manageable. Common algorithms that use heuristics include A* algorithm, depth-first search (DFS) with heuristics, breadth-first search (BFS) with heuristics, and hill climbing.

Methodology for Calculating Heuristic Values:

Euclidean Distance:
- Identify coordinates of the start and goal states.
- Use the formula provided to calculate the straight-line distance.
Manhattan Distance:
- Assess the number of vertical and horizontal moves needed to align each tile with its goal position.
Number of Misplaced Tiles:
- Count how many tiles are not in their correct position compared to the goal state.