05_uninformed_search2013

05_uninformed_search2013 - Navigating through a search tree...

Info iconThis 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
Background 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
Background 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
Background 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.
Background 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 ]}

Page1 / 13

05_uninformed_search2013 - Navigating through a search tree...

This preview shows document pages 1 - 5. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online