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Uniformed tree searching


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Uniformed Tree Searching Algorithms …

Uniformed Tree Searching Algorithms
Depth First Search
Breadth First Search
Uniform Cost Search

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  • 1. Uniformed Tree Searching Prepared by Aya Elshiwi Supervisor Dr.Eman Eldaydamony
  • 2. Tree searching A B D C E F H L M I N O G J P K Q • A tree search starts at the root and explores nodes from there, looking for a goal node (a node that satisfies certain conditions, depending on the problem) • For some problems, any goal node is acceptable (N or J); for other problems, you want a minimum-depth goal node, that is, a goal node nearest the root (only J) Goal nodes 2
  • 3. Search Algorithms Uninformed vs. informed Search algorithms differ by how they pick which node to expand. • Uninformed search Algorithms – In making this decision, these look only at the structure of the search tree and not at the states inside the nodes. – Also known as “blind search”. – Uninformed search methods: Breadth-first, depth-first, depthlimited, uniform-cost, depth-first iterative deepening, bidirectional. • Informed search Algorithms – In making this decision , these look at the states inside the nodes. – Also known as “heuristic search”. – Informed search methods: Hill climbing, best-first, greedy search, beam search, A, A* 3
  • 4. Example Uniformed (blind) search Informed (heuristic) search 4
  • 5. Evaluating Search Strategies Strategies are evaluated along the following dimensions: – Completeness: Does it always find a solution if one exists? – Time complexity: How long does it take to find a solution ? – Space complexity: How much memory is needed to perform the search? – Optimality: Does the strategy find the optimal solution? 5
  • 6. Uniformed search Algorithms • Depth First Search [DFS] • Breadth First Search [BFS] • Uniform Cost Search [UCS] 6
  • 7. Depth-first searching • A depth-first search (DFS) explores a path all the way to a leaf before backtracking and exploring another path. Animated Graphic • The search proceeds immediately to the deepest level of the search tree, where the nodes have no successors . As those nodes are expanded , they are dropped from the Frontier [stack] ,so then the search “backs up” to the deepest next node that still has unexplored successors . • Use Stack data structure [FILO] as a frontier . 7
  • 8. How to do depth-first searching A Example B Algorithm Put the root node on a stack; while (stack is not empty) { remove a node from the stack; if (node is a goal node) return success; put all children of node onto the stack; } return failure; D C E F H L M I N O G J P K Q Animated Graphic • • • For example, after searching A, then B, then D, the search backtracks and tries another path from B Node are explored in the order A B D E H L M N I OPCFGJKQ N will be found before J 8
  • 9. Example 9
  • 10. Properties of Depth First Algorithm b( branching factor) : Maximum number of successors of any node. m: maximal depth of a leaf node • • • • • Number of nodes generated (worst case): 1 + b + b2 + … + bm = O(bm) Complete: only for finite search tree Optimal : Not optimal Time complexity: O(bm) Space complexity : O(bm) [or O(m)] 10
  • 11. Breadth-first searching • A breadth-first search (BFS) is a simple strategy in which the root node is expanded first , then all the successors of the root node , then their successors. • Animated Graphic In general , all the nodes are expanded at a given depth in the search tree before any nodes at the next level are expanded. • Use a queue data structure [FIFO]. 11
  • 12. How to do breadth-first searching A Algorithm • • • • B Enqueue the root node Dequeue a node and examine it D – If the element sought is found in this node, quit the search and return a result. – Otherwise enqueue any successors (the direct child nodes) that have not yet been discovered. If the queue is empty, every node on the graph has been examined – quit the search and return "not found". If the queue is not empty, repeat from Step 2. C E F H L M I N O G J P K Q Animated Graphic • For example, after searching A, then B, then C, the search proceeds with D, E, F, G • Node are explored in the order A B C D E F G H I J K L M N O12 Q P
  • 13. Properties of breadth-first search b: branching factor d: depth of shallowest goal node Total number of nodes generated is: 1+b+b2+b3+… … + bd = O(bd ) • Complete Yes (if b is finite) • Time 1+b+b2+b3+… … + bd + b(bd -1) = O(bd+1) Because the whole layer of nodes at depth d would be expanded before detecting the goal . • Space O(bd) (keeps every node in memory) • Optimal if all operators have the same cost. 13
  • 14. Depth- vs. breadth-first searching • When a breadth-first search succeeds, it finds a minimum-depth (nearest the root) goal node. • When a depth-first search succeeds, the found goal node is not necessarily minimum depth. • For a large tree, breadth-first search memory requirements may be excessive. • For a large tree, a depth-first search may take an excessively long time to find even a very nearby goal node. 14
  • 15. Uniform cost search • Find the least-cost goal . • The search begins at the root node. The search continues by visiting the next node which has the least total cost from the root. Nodes are visited in this manner until a goal state is reached. • Each node has a path cost from start (= sum of edge costs along the path). Expand the least cost node first. • Equivalent to breadth-first if step costs all equal • Use a priority queue (ordered by path cost) instead of a normal queue . Animated Graph 15
  • 16. Pesudocode procedure UniformCostSearch(Graph, root, goal) node := root, cost = 0 frontier := priority queue containing node only explored := empty set Do if frontier is empty return failure node := frontier.pop() if node is goal return solution explored.add(node) for each of node's neighbors n if n is not in explored if n is not in frontier frontier.add(n) else if n is in frontier with higher cost replace existing node with n 16
  • 17. How to do Uniform Cost Search Algorithm if - Frontier : empty >Return fail else - Add node to frontier. - Check: node (goal)>solution - Add node to explored. - Neighbor s: if not explored >add to frontier - Else :if was with higher cost replace it . Example Solution Explored : A D B E F C path: A to D to F to G Cost = 8 17
  • 18. Properties of uniform cost search - Equivalent to breadth-first if step costs all equal. • Complete Yes .assuming that operator costs are nonnegative (the cost of path never decreases) • Optimal Yes. Returns the least-cost path. 18
  • 19. Reference • Solving problems by searching : • Depth-First search : • Breadth-First search: • Uniform cost search : • Depth First & Breadth First: 19
  • 20. Questions? 20