Adhit_presentation_Searching_Algorithm(BFS,DFS).pptx

Adhit_presentation_Searching_Algorithm(BFS,DFS).pptx

• 1.
  Problem Solving By Searching -AdhitUpadhyay -1203
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  Problem Solving BySearching • Problem solving in AI is a systematic search through a range of possible action in order to reach some predefined goal or the solution. • There are four general states in problem solving which are: • Goal Formulation: • A goal is a state that the agent is trying to reach • Problem Formulation: • This is the process of deciding what actions and states to consider, given goal • Search Method: • It determines possible sequence of actions and choose the best one. • Execute: • Provides the solution to the action.
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  Problem Formulation • Itinvolves deciding what actions and states to consider, given the goal. • A problem can be defined formally by 4 components : • An initial state: From which agent start • State description: Description of possible action available on agent. Also called as successor function. • Goal test: Determining the state is goal state or not. • Path cost: Sum of cost of each path from initial to given state
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  State Space Representation •The state space is defined as the directed graph with each node is a state and arc represents the application of an operator transforming a state to a successor state. • A solution is path from the initial state to goal state consisting of (S,s,O,G) where: S = Set of state s = start state O = Operation (which helps to transfer state to it’s successor state) G = Goal state
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  Search Techniques /Search Strategies • There are two broad search strategies: • Uninformed search methods: • In this method, the order in which potential solution path are considered is arbitrary, using no domain specific information to judge where the solution is likely to lie. • Example: Breadth First Search, Depth First Search, Depth Limit Search, Bidirectional Search, etc. • Heuristically informed search methods: • In this method, one uses domain- dependent (heuristic )information in order to search the space more efficiently. • Example: Greedy best first search, A* search, Hill climbing search, etc.
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  Performance Evaluation ofSearch Technique • We can evaluate the performance of search techniques in four ways: • Completeness : An algorithm is said to be complete if it definitely finds solution to the problem, if exist. • Time complexity: How long does it take to find a solution? • Space complexity: How much space is used by the algorithm? • Optimality: If a solution is found, is it guaranteed to be an optimal one ? Example: Is it the one with minimum cost ?
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  A. Breadth FirstSearch(BFS): • It is an algorithm for traversing or searching tree or graph data structure. • It stats with the tree root and explores all the neighbor nodes at the present depth prior to moving on the nodes at the next depth level. • It is implemented in FIFO queue in DSA. • First move horizontally and visit all the nodes of the current layer • Move to the next layer • Do not generate as child node if the node is already parent to avoid more loop.
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  Performance Measure ofBFS • Completeness: It always finds the solution if exist. • Time complexity : Assume every state has b successor then, • O(n) = O( • Space complexity: If the goal node is at depth d then nodes of d depth are expanded before traversing so space complexity is O( . • Optimal: If all the path has same cost then optimal otherwise not.
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  Applications • Social NetworkingWebsites use BFS. • In GPS navigation system. • Broadcasting in network.
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  B. Depth FirstSearch (DFS): • It is another method for search or traversing tree or graph data structures. • This algorithm stats at root node and explores as far as possible along each branch before backtracking. • It is implemented in LIFO stack. • The main strategy of DFS is to explore deeper into the graph whenever possible. • When all the edge have been explored, the search backtracks until it reaches an unexplored neighbor. • This process continues until all the vertices are reached without forming any loop.
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  Performance Measure ofDFS • Completeness: It does not find the solution in all cases. • Time complexity : Let m be the maximum depth then, • O(n) = O( • Space complexity: Need to store single node so, • = (b + b + b + b + ….) * m time • = O (bm) • Optimal: It gives optimal solution than BFS but it does not give optimal solution if the foal node is in right child of level 2 or 3 of graph having m = 10 or more.
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  Applications • Detecting cyclein the graph • Path Finding • Topological sorting