05_uninformed_search2013

05_uninformed_search2013 - Navigating through a search tree...

Info icon This preview shows pages 1–5. Sign up to view the full content.

View Full Document Right Arrow Icon
9/20/13 1 Uninformed Search Russell and Norvig 3 rd ed. chap. 3.3-3.4 Navigating through a search tree A B C D E F G H I J L K Navigating through a search tree A Navigating through a search tree A B C
Image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
9/20/13 2 Navigating through a search tree A B C D E Navigating through a search tree A B C D E F G Navigating through a search tree A B C D E F G H I Navigating through a search tree A B C D E F G H I J
Image of page 2
9/20/13 3 Navigating through a search tree A B C D E F G H I J L K Navigating through a search tree A B C D E F G H I J L K Unexpanded nodes: fringe/frontier A B C D E F G H I At every point in the search process we keep track of a list of nodes that haven’t been expanded yet: the frontier Tree search A function TREE-SEARCH( problem, strategy ) return a solution or failure Initialize the frontier using the initial state of the problem loop do if the frontier is empty then return failure choose leaf node for expansion using strategy and remove from frontier if node contains goal state then return solution else expand the node and add resulting nodes to the frontier Initial state
Image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
9/20/13 4 Tree search A B C D E F G function TREE-SEARCH( problem, strategy ) return a solution or failure Initialize the frontier using the initial state of the problem loop do if the frontier is empty then return failure choose leaf node for expansion using strategy and remove from frontier if node contains goal state then return solution else expand the node and add resulting nodes to the frontier What’s in a node n State n Parent n Action (the action that got us from the parent) n Depth n Path-Cost Metrics for comparing search strategies n A strategy is defined by the order of node expansion. n Problem-solving performance is measured in four ways: q Completeness: Does it always find a solution if one exists? q Optimality: Does it always find the least-cost solution? q Time Complexity: Number of nodes generated/expanded. q Space Complexity: Number of nodes stored in memory during search. n Time and space complexity are measured in terms of: q b - maximum branching factor of the search tree q d - depth of the least-cost solution q m - maximum depth of the state space (may be ) Uninformed search strategies n a.k.a. blind search = use only information available in problem definition.
Image of page 4
Image of page 5
This is the end of the preview. Sign up to access the rest of the document.

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern