Summary of "Iterative Deepening Depth First Search in AI || IDDFS in AI"

The video discusses the concept of Iterative Deepening Depth First Search (IDDFS) in artificial intelligence, explaining its methodology, advantages, and disadvantages compared to other search algorithms.

Main Ideas and Concepts:

Definition of IDDFS:
- IDDFS combines the depth-first search (DFS) and breadth-first search (BFS) strategies.
- It iteratively increases the search depth limit, allowing the algorithm to explore deeper levels without predefining a maximum depth.
Working Mechanism:
- The algorithm starts at level zero and explores all nodes at that level.
- It then increments the depth limit and repeats the process until it finds the goal node.
- This approach allows for reaching goals that might be beyond a previously set depth limit.
Space Efficiency:
- IDDFS is space-efficient because it only stores nodes in the current depth level, unlike BFS which stores all nodes at all levels.
- It effectively uses a stack data structure (LIFO) to manage nodes during the search.
Search Process:
- The algorithm uses a Last In, First Out (LIFO) method to explore child nodes.
- Nodes are pushed onto the stack, and the algorithm backtracks when it reaches the depth limit or when there are no child nodes to explore.
Advantages:
- Automatically increasing depth allows the algorithm to find solutions that might be missed with a fixed depth limit.
- Space efficiency compared to BFS.
Disadvantages:
- The process can be slower because it may revisit nodes multiple times, leading to higher time complexity.
- It restarts from the initial node at each iteration, which can be inefficient.

Methodology:

Step-by-Step Process:
- Start at level 0 and explore all nodes.
- Increment the depth limit by 1.
- Use a stack to manage nodes (LIFO).
- Explore child nodes and backtrack when necessary.
- Repeat until the goal node is found.