Uninformed Search technique
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This document discusses several search strategies including uninformed search, breadth-first search, depth-first search, uniform cost search, iterative deepening search, and bi-directional search. It provides algorithms and examples to explain how each strategy works. Key points include: breadth-first search visits nodes by level of depth; depth-first search generates nodes along the largest depth first before moving up; uniform cost search expands the lowest cost node; and iterative deepening search avoids infinite depth by searching each level iteratively and increasing the depth limit.
Uninformed Search technique
- 1. Uninformed Search Technique Presented By: Kapil Dahal kapil.dahal@kcc.edu.np
- 2. Breadth First Search • The breadth first search algorithm visits the nodes of the tree along its breadth, starting from the level with depth 0 to the maximum depth. • Here, the nodes in the tree are traversed following their ascending ordered labels.
- 4. Algorithm
- 5. Contd….
- 6. How the algorithm works? • If the current node is not the goal add the offspring of the current in any order to the rear end of the queue and redefine the front element of the queue as the current. • The algorithm terminates, when the goal is found.
- 8. Expanding….
- 9. Time and Memory requirement : Example
- 10. Uniform Cost • Breadth first search is optimal when all step costs are equal. • Uniform Cost search is optimal when step costs varies. • Uniform-cost search expands the node n with lowest path cost.
- 12. Depth First Search • The depth first search generates nodes and compares them with the goal along the largest depth of the tree and moves up to the parent of the last visited node, only when no further node can be generated below the last visited node. • After moving up to the parent, the algorithm attempts to generate a new offspring of the parent node. • Uses Stack as data structure.
- 13. Algorithm
- 14. Example tree
- 15. Contd… • In the above algorithm, a starting node is placed in the stack, the top of which is pointed to by the stack-top. For examining the node, it is popped out from the stack. • If it is the goal, the algorithm terminates, else its children are pushed into the stack in any order. • The process is continued until the stack is empty.
- 16. example:DFS
- 17. Properties: Depth first search
- 18. Depth Limited Search • Depth-limited search avoids the pitfalls of depth-first search by imposing a cutoff on the maximum depth of a path. • This cutoff can be implemented with a special depth-limited search algorithm, or by using the general search algorithm with operators that keep track of the depth. • To avoid the infinite depth problem of DFS, we can decide to only search until depth L, i.e. we don’t expand beyond depth L.
- 19. Iterative deepening depth-first search • To avoid the infinite depth problem of DFS, we can decide to only search until depth L, i.e. we don’t expand beyond depth L ->Depth first search. • What of solution is deeper than L? -->Increase L iteratively. ->Iterative Deepening Search
- 20. Contd… • When the initial depth cut-off is one, it generates only the root node and examines it. • If the root node is not the goal, then depth cut-off is set to two and the tree up to depth 2 is generated using typical depth first search. • Similarly, when the depth cut-off is set to m, the tree is constructed up to depth m by depth first search.
- 21. Contd… • Iterative deepening search is a strategy that sidesteps the issue of choosing the best depth limit by trying all possible depth limits: first depth 0, then depth 1, then depth 2, and so on.
- 22. Algorithm: IDS
- 23. Example : Iterative deepening
- 24. Nodes Generated:IDS • Number of nodes generated in a depth-limited search to depth d with branching factor b: • Number of nodes generated in an iterative deepening search to depth d with branching factor b:
- 25. Properties: IDS
- 26. Bi-Directional Search • Alternate searching from the start state toward the goal and from the goal state toward the start. • Stop when the frontiers intersect. • Works well only when there are unique start and goal states. • Requires the ability to generate “predecessor” states. •