PPT-Depth First Search

Maedeh Mehravaran Big data 1394 Depth First Search DFS Starts at the source vertex When there is no edge to unvisited node from the current node backtrack to most recently visited node with unvisited neighbors. Adjacency Lists. A: F G. B: A H. C: A D. D: C F. E: C D G. F: E. :. G: . :. H: B. :. I: H. :. F. A. B. C. G. D. E. H. I. 2. Directed Depth First Search. F. A. B. C. G. D. E. H. I. dfs(A). A-F A-G. Jim Little. UBC CS 322 – Search . 2. September . 12, . 2014. Textbook . §. 3.5. 1. CPSC 322, Lecture 4. Slide . 2. Search. is a key computational mechanism in many . AI agents . We will study the basic principles of search on the simple . Approximating the . Depth. via Sampling and Emptiness. Approximating the . Depth. via Sampling and Emptiness. Approximating the . Depth. via Sampling and Emptiness. Example: Range tree. S = Set of points in the plane. Lecture 14. 1. CS473-Algorithms I. Lecture 15. Graph Searching: . Depth-First Search. and . Topological Sort. CS 473. Lecture 14. 2. Depth-First Search. Graph . G. (. V. ,. E. ) directed or undirected. Depth First Search (DFS). We Already Covered . Breadth First Search(BFS). Traverses the graph one level at a time. Visit all outgoing edges from a node before you go deeper. Needs a queue. BFS creates a tree called BFS-Tree. CS344 : AI - Seminar. 20. th. January 2011. TL Nishant Totla, RM Pritish Kamath. M1 Garvit Juniwal, M2 Vivek Madan. guided by. Prof. Pushpak Bhattacharya. Outline. Two player games. Game trees. MiniMax algorithm. Problem-solving agents. Example: Romania. On holiday in Romania; currently in Arad. .. Flight leaves tomorrow from . Bucharest. What do we need to define?. Problem Formulation. The process of defining actions, states and goal.. Depth First Search (DFS). We Already Covered . Breadth First Search(BFS). Traverses the graph one level at a time. Visit all outgoing edges from a node before you go deeper. Needs a queue. BFS creates a tree called BFS-Tree. Artificial Intelligence. Dr. Daniel Tauritz (Dr. T). Department of Computer Science. tauritzd@mst.edu. http://web.mst.edu/~tauritzd/. general course website: . http://web.mst.edu/~tauritzd/courses/intro_AI.html. Artificial Intelligence. Dr. Daniel Tauritz (Dr. T). Department of Computer Science. tauritzd@mst.edu. http://web.mst.edu/~tauritzd/. general course website: . http://web.mst.edu/~tauritzd/courses/intro_AI.html. William Lam. Final Defense. March 16, 2017. Committee:. Rina . Dechter. Alexander . Ihler. Sameer Singh. Collaborators: Rina . Dechter. Kalev. . Kask. Javier . Larrosa. Alexander . Ihler. Thesis Contributions. Artificial Intelligence. Dr. Daniel Tauritz (Dr. T). Department of Computer Science. tauritzd@mst.edu. http://web.mst.edu/~tauritzd/. general course website: . http://web.mst.edu/~tauritzd/courses/intro_AI.html. (Section 3.4). Source: . Fotolia. Uninformed search strategies. A . search strategy . is defined by picking the order of node . expansion. Uninformed. . search strategies use only the information available in the problem . In addition to specifying a search direction (data-driven or goal-driven), a search algorithm must determine the order in which states are examined in the tree or the graph. This section considers two possibilities for the order in which the nodes of the graph are considered: .