1. Iterative Deepening Search
Ashis Kumar Chanda
Department of Computer Science and Engineering
University of Dhaka

2. Topics
• Introduction
• Searching Methods
• Iterative Deepening Search
• Properties
• Analysis
• Summary
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3. Introduction
• Search an item in Graph
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Air flight system
• Each city represents a vertex
• Each direct flight represents an edge

4. Graph
• A well recognized tool in modeling problems
• Consist of:
– Vertices can be considered locations.
– Edges represent connections connections.
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D
E
A
C
F
B
Vertex
Edge

5. Graph Representation
• How will we represent a graph in computer?
1. Adjacency Matrix
2. Use a 2D matrix to represent the
graph
3. Adjacency List
4. Use a 1D array of linked lists
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6. Adjacency Matrix
A[i][j] = 1 if there is an edge connecting vertices i,j
A[i][j] = 0 otherwise
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7. Adjacency List
The adjacency list is an array A[0..n-1] of lists,
where n is the number of vertices in the graph
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8. Searching Methods
• Most common methods
– Breadth First Search (BFS)
– Depth First Search (DFS)
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9. Breadth-first search
• Span search area in each level
• FIFO structure (queue)
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10. Depth-first search
• Move at depth, start search and then return at back
• LIFO structure (Stack)
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11. Any New Idea?
• What's wrong with DFS?
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Iterative Deepening Search
DFS can move into an infinite loop
DFS is neither complete nor optimal

12. Iterative Deepening Search
• IDS is similar to DFS
• Depth is not known
• increasing the depth limit with each iteration
until it reaches d, the depth of the goal state
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13. Iterative deepening search l =0
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14. Iterative deepening search l =1
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15. Iterative deepening search l =2
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16. Iterative deepening search l =3

17. Pseudo-code
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Input: Graph root, goal
Output: solution or failure
IDS(root, goal)
{
for(i=0 to infinite)
{
DLS(root, goal, depth=i)
}
}
depth-limited DFS (called DLS);

18. Properties
where b is the branching factor and d is the depth of goal
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Criteria Properties of IDF
Complete search Yes
Required Time O(bd )
Memory Space O(bd)
Optimal Solution Yes

19. Analysis
• Applicable when there is a large search space
and depth is not known
• The main advantage of IDS in game tree
searching
• IDS combines depth first search’s space
efficiency and breadth first search’s complete
-ness
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20. Practice Problem
• Show each steps to find node F from the
source node Y using BFS, DFS, IDS
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21. Summary
• Representation of Graph model
• Searching model BFS, DFS
• Iterative Deepening Search Model is
combination of BFS, DFS
• Better than DFS and needs less space than BFS
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22. About Me
• Research Topics:
- Flexible Periodic Pattern Mining
- Closed Frequent Pattern Mining
• ML course of Stanford University
• Won several Software Contests
• Solve Hundreds ACM problem
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Story writing Code Rage Badge
www.chandacse.blogspot.com

23. References
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- Artificial Intelligence a Modern Approach
by Russell and Norvig
- Introduction to Algorithm by Cormen
- Fundamentals of Computer Algorithms by
Ellis; Sahni, Sartaj Horowitz
- http://en.wikipedia.org/wiki/Iterative_deepening_depth-
- http://artint.info/html/ArtInt_62.html
- http://will.thimbleby.net/algorithms/doku.php?id=iterati