A* (A Star) Search Algorithm with Solved Example in Artificial Intelligence by Dr. Mahesh Huddar

Summary

In this video, the A* search algorithm is explained, highlighting its use in artificial intelligence to find optimal paths. The algorithm combines two factors: the actual cost (g(n)) from the starting point and the heuristic (h(n)) estimating the cost to the goal. Through a practical example, the video demonstrates how the algorithm selects successor nodes by evaluating the sum of these two components. The process continues until the goal is reached, showcasing how A* makes better decisions than algorithms relying solely on immediate costs.

Takeaways

• 😀 A* Search Algorithm was implemented in 1968 and is used to find the most efficient path between a start node and a goal node.
• 😀 The A* algorithm uses two main components: g(n), the actual cost from the start node to the current node, and h(n), the estimated cost from the current node to the goal.
• 😀 The total cost function f(n) = g(n) + h(n) is used to evaluate potential successor nodes and select the most efficient path.
• 😀 Unlike other algorithms that focus only on immediate costs, A* incorporates future potential (heuristic values) for more informed decision-making.
• 😀 Heuristic values represent the estimated distance from any given node to the goal node, helping A* prioritize nodes that are likely to lead to the goal more efficiently.
• 😀 The algorithm starts at the source node, evaluates potential paths to successors, and chooses the one with the lowest f(n) value.
• 😀 In the example, the source node (s) has several successor nodes (a, d), and A* calculates the f(n) for each to determine which node to expand first.
• 😀 A* continuously calculates f(n) for neighboring nodes, expanding the node with the lowest value until the goal node is reached.
• 😀 The path chosen by A* is guaranteed to be the most cost-effective, considering both immediate costs and estimated future costs.
• 😀 The final path from source to goal node (s → a → b → d → e → f → g) is determined by iteratively selecting nodes with the lowest f(n).

Q & A

What is the A* search algorithm?

The A* search algorithm is an informed search algorithm used in artificial intelligence to find the shortest path between an initial node and a goal node. It combines both actual and estimated costs to guide the search efficiently.

When was the A* search algorithm first implemented?

The A* search algorithm was implemented in 1968.

What is the equation used in the A* search algorithm?

The equation used in A* search is f(n) = g(n) + h(n), where g(n) is the cost from the initial node to the current node, and h(n) is the heuristic or estimated cost from the current node to the goal node.

How does A* differ from other search algorithms?

Unlike other search algorithms that consider only the immediate or actual cost, A* considers both the actual cost (g(n)) and the estimated future cost (h(n)), allowing it to make more informed decisions about which path to follow.

What is meant by 'heuristic value' in the context of A* search?

The heuristic value, denoted as h(n), represents the estimated distance or cost from a node n to the goal node. It helps guide the algorithm toward the goal efficiently.

What information is needed to apply the A* search algorithm to a problem?

To apply A*, you need the actual cost between connected nodes and the heuristic value for each node, which estimates the distance from that node to the goal.

In the given example, what are the source and goal nodes?

In the example, 'S' is the source node and 'G' is the goal node.

How is the next node selected in the A* search process?

For each successor node, the value of f(n) = g(n) + h(n) is calculated, and the node with the smallest f(n) value is selected as the next node to expand.

What is the final path obtained in the example discussed?

The final path from the source node S to the goal node G is S → D → E → F → G.

Why is A* considered an efficient search algorithm?

A* is considered efficient because it combines both actual and heuristic costs, which allows it to find the optimal path while minimizing the number of nodes explored.

What does g(n) represent in the A* equation?

g(n) represents the actual cost or distance from the initial node to the current node n.

What happens when the algorithm reaches the goal node?

When the algorithm reaches the goal node, the search stops, and the path from the source to the goal node is returned as the solution.