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Uniformed search techniques are algorithms used in computer science and artificial intelligence to systematically explore a search space without any additional information about the location of the goal. These techniques are typically used in problems like pathfinding, puzzle solving, and optimization where the structure of the search space is known but the goal state is not.

Here are some common uniformed search techniques:

  1. Breadth-First Search (BFS):
    • Explores all the neighbor nodes at the present depth before moving on to the nodes at the next depth level.
    • Implemented using a FIFO (First-In-First-Out) queue data structure.
  2. Depth-First Search (DFS):
    • Explores as far as possible along each branch before backtracking.
    • Implemented using a LIFO (Last-In-First-Out) stack data structure.
  3. Depth-Limited Search (DLS):
    • Similar to DFS, but with a depth limit to prevent going too deep and getting stuck in infinite loops.
    • Helpful for problems with infinite or very deep search spaces.
  4. Iterative Deepening Depth-First Search (IDDFS):
    • A combination of BFS and DFS.
    • Performs multiple DFS with increasing depth limits until the goal is found.
    • It combines the space efficiency of DFS and the completeness of BFS.
  5. Uniform Cost Search (UCS):
    • Expands nodes based on their path cost from the initial state rather than their depth.
    • Always selects the lowest cost node to expand next.
    • Implemented using a priority queue ordered by path cost.
  6. Bidirectional Search:
    • Simultaneously performs two parallel searches, one from the initial state and the other from the goal state.
    • Terminates when the two searches meet in the middle.
    • Typically more efficient than traditional searches, especially for large search spaces.
  7. Greedy Best-First Search:
    • Expands the node that is closest to the goal based on a heuristic function.
    • Heuristic function estimates the cost from the current node to the goal.
    • Not guaranteed to find the optimal solution but often efficient in practice.
  8. A Search*:
    • A combination of UCS and greedy best-first search.
    • Evaluates nodes based on the sum of their path cost from the initial state and the estimated cost to the goal.
    • Uses a heuristic function to guide the search towards the goal while also considering the cost incurred so far.
    • Admissible heuristic ensures optimality.

These are foundational techniques used in various problem-solving domains and provide the basis for more advanced algorithms in artificial intelligence and computer science. Choosing the appropriate technique depends on the problem characteristics, such as the size of the search space, the presence of constraints, and the availability of domain knowledge.