Heuristic Search Algorithm in AI
Heuristic search algorithms leverage heuristic functions to make more intelligent decisions during the search process. Some common heuristic search algorithms include:
A* Algorithm
The A* algorithm is one of the most widely used heuristic search algorithms. It uses both the actual cost from the start node to the current node (g(n)) and the estimated cost from the current node to the goal (h(n)). The total estimated cost (f(n)) is the sum of these two values:
[Tex]f(n) = g(n) +h(n)[/Tex]
Greedy Best-First Search
The Greedy Best-First Search algorithm selects the path that appears to be the most promising based on the heuristic function alone. It prioritizes nodes with the lowest heuristic cost (h(n)), but it does not necessarily guarantee the shortest path to the goal.
Hill-Climbing Algorithm
The Hill-Climbing algorithm is a local search algorithm that continuously moves towards the neighbor with the lowest heuristic cost. It resembles climbing uphill towards the goal but can get stuck in local optima.
Heuristic Function In AI
Heuristic functions play a critical role in artificial intelligence (AI), particularly in search algorithms used for problem-solving. These functions estimate the cost to reach the goal from a given state, helping to make informed decisions that optimize the search process.
In this article, we will explore what heuristic functions are, their role in search algorithms, various types of heuristic search algorithms, and their applications in AI.
Table of Content
- What are Heuristic Functions?
- Search Algorithm
- Heuristic Search Algorithm in AI
- A* Algorithm
- Greedy Best-First Search
- Hill-Climbing Algorithm
- Role of Heuristic Functions in AI
- Common Problem Types for Heuristic Functions
- Path Finding with Heuristic Functions
- Step 1: Define the A* Algorithm
- Step 2: Define the Visualization Function
- Step 3: Define the Grid and Start/Goal Positions
- Step 4: Run the A* Algorithm and Visualize the Path
- Complete Code
- Applications of Heuristic Functions in AI
- Conclusion