Explain heuristic search in AI with examples.

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A heuristic search in AI is a problem-solving technique that uses heuristics (rules of thumb, estimates, or domain knowledge) to guide the search process toward the best solution more efficiently than brute-force search.

🔑 Key Idea

• A heuristic function (h(n)) estimates the cost or distance from a given state n to the goal.

• Instead of exploring all possible paths (like uninformed search), the algorithm prioritizes states that seem “closer” to the goal.

• This reduces computation and makes the search faster.

🧩 Examples of Heuristic Search

1. Best-First Search

   • Expands the node that appears closest to the goal based on h(n).

   • Example: In a map, always choose the road leading most directly toward the destination.

2. A Search*

   • Combines actual path cost g(n) and estimated cost h(n) into f(n) = g(n) + h(n).

   • Guarantees the optimal path if h(n) is admissible (never overestimates).

   • Example: Finding the shortest driving route in GPS navigation.

3. Greedy Best-First Search

   • Chooses paths with the lowest heuristic value, ignoring past cost.

   • Example: Maze solving where you always move closer to the exit.

📌 Real-World Applications

• Pathfinding: GPS systems use A* to calculate shortest routes.

• Games: Chess AI uses heuristics to evaluate board positions.

• Robotics: Robots use heuristics for obstacle avoidance and motion planning.

👉 In summary:
Heuristic search in AI uses informed guesses to guide problem-solving, making search processes faster and more efficient. Algorithms like A* and Greedy Best-First Search are common examples.

Read More:

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