Define search algorithms used in planning agents.

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In planning agents (commonly studied in Artificial Intelligence), search algorithms are fundamental tools that explore possible states and actions to find a sequence of steps (a plan) that achieves a goal from a given initial state. These algorithms differ in how they explore the state space, their efficiency, and their guarantees of finding optimal solutions.

🔹 1. Uninformed Search (Blind Search)

These algorithms do not use domain knowledge; they only rely on the problem definition.

  • Breadth-First Search (BFS): Explores level by level, guarantees the shortest solution if all step costs are equal.

  • Depth-First Search (DFS): Explores one branch deeply before backtracking; memory-efficient but may get stuck in deep paths.

  • Uniform Cost Search (UCS): Expands the node with the lowest path cost, useful when costs vary.

🔹 2. Informed Search (Heuristic Search)

These use heuristics (estimates of cost-to-goal) to guide the search.

  • Greedy Best-First Search: Chooses the path that appears closest to the goal based on the heuristic, but not always optimal.

  • A* Search: Balances cost-so-far and heuristic estimate; finds the optimal solution if the heuristic is admissible (never overestimates).

  • Iterative Deepening A* (IDA*): Uses A* logic but with depth limits, saving memory.

🔹 3. Local Search Algorithms

Instead of exploring the entire state space, these optimize iteratively.

  • Hill Climbing: Moves to the neighbor with the best improvement; fast but may get stuck in local maxima.

  • Simulated Annealing: Similar to hill climbing but allows occasional "bad moves" to escape local maxima.

  • Genetic Algorithms: Use evolutionary strategies (selection, crossover, mutation) to evolve solutions.

🔹 4. Specialized Planning Search

  • Forward State-Space Search: Start from initial state and apply actions forward until the goal is reached.

  • Backward (Regression) Search: Start from the goal and work backward by finding actions that could produce it.

  • Heuristic Planning (like GraphPlan, SATPlan): Build planning graphs or reduce planning to satisfiability problems for efficiency.

✅ In short:
Planning agents use uninformed search (BFS, DFS, UCS) when no knowledge is available, informed search (A*, Greedy) when heuristics are known, local search (hill climbing, simulated annealing) for optimization, and specialized planning searches (forward/backward, GraphPlan) tailored to planning problems.

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