Search Algorithms in AI - Types of Search Algorithms in AI & Techniques

What are Search Algorithms in AI?

Search algorithms help AI systems explore possible states to find a path from an initial state to a goal state — fundamental to problem-solving agents.

Uninformed Search Techniques

• Breadth-First Search (BFS): Explores all nodes at the current depth before moving deeper.
• Depth-First Search (DFS): Explores as far as possible along a branch before backtracking.
• Uniform Cost Search: Expands the node with the lowest path cost.

Informed (Heuristic) Search Techniques

• Greedy Best-First Search: Chooses the node closest to the goal based on a heuristic.
• A* Search: Combines path cost and heuristic estimate for optimal, efficient search.

Local Search Algorithms

• Hill Climbing: Continuously moves toward increasing value.
• Simulated Annealing: Allows occasional worse moves to avoid local optima.

Applications

Search algorithms power route planning (like GPS navigation), puzzle solvers, and game-playing AI such as chess engines.