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IDDFS Algorithm in AI

 



IDDFS Algorithm in AI

Iterative Deepening Depth-First Search (IDDFS) is an uninformed search algorithm used in artificial intelligence and computer science to traverse or search a tree or graph. It is a hybrid strategy that combines the memory efficiency of Depth-First Search (DFS) with the completeness and optimality of Breadth-First Search (BFS).

How IDDFS Works

The core idea of IDDFS is to repeatedly perform a depth-limited DFS with a progressively increasing depth limit. The process can be broken down into these steps:

  1. Start with a Depth Limit of 0: The algorithm begins by performing a DFS on the root node with a depth limit of zero. It only checks if the root is the goal node.

  2. Increase the Depth Limit: If the goal is not found, the depth limit is increased by one.

  3. Repeat Depth-Limited Search: A new depth-limited DFS is then performed from the root, this time exploring all nodes up to the new depth limit.

  4. Iterate Until Found: This process of increasing the depth limit and restarting the DFS continues until the goal node is found or the entire search space has been explored.

While this may seem inefficient because nodes at shallower depths are visited multiple times, the time cost is often not as significant as it appears. In a search tree with a high branching factor, the vast majority of nodes are at the deepest level. The repeated exploration of the shallower nodes is a relatively small overhead compared to the search at the final, deepest level.

Key Characteristics

  • Completeness: IDDFS is complete for finite graphs. This means that if a solution exists, the algorithm is guaranteed to find it. This overcomes a major drawback of a simple DFS, which can get trapped in an infinite path and never find a solution.

  • Optimality: IDDFS is optimal for unweighted graphs, meaning it finds the shortest path to a goal node in terms of the number of edges. This is because it explores the search space level by level, guaranteeing that the first goal it finds is at the shallowest possible depth.

  • Space Complexity: The space complexity of IDDFS is excellent, similar to DFS, typically O(d) where d is the depth of the solution. It only needs to store the current path of nodes being explored, which is a stack of size equal to the current depth limit. This is a significant advantage over BFS, which may require exponential space in the worst case to store all nodes at a given level.

Advantages and Disadvantages

Advantages

  • Optimal and Complete: It finds the shallowest solution and is guaranteed to find a solution if one exists in a finite graph.

  • Memory Efficient: It uses very little memory, making it suitable for large or infinite search spaces where BFS would be infeasible due to memory constraints.

  • Responsive: Because early iterations are very fast, it can provide a quick, though not necessarily optimal, solution, which can be useful in time-sensitive applications like game-playing programs.

Disadvantages

  • Repeated Work: The main drawback is the redundant work of re-exploring nodes at shallower depths in each new iteration.

  • Not Suitable for All Graphs: In graphs with non-uniform edge costs, IDDFS does not guarantee the shortest path in terms of total cost, only the path with the fewest edges.

In conclusion, IDDFS is a powerful and practical uninformed search algorithm that provides a great balance between the time efficiency of BFS and the space efficiency of DFS, making it a preferred choice when the search space is large and the solution depth is unknown.

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