Thursday Jan 24 2013 - Lecture 6

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Department
Computing and Information Science
Course
CIS 3700
Professor
Yang Xiang
Semester
Winter

Description
Thursday, January 24 2013 CIS 3700 Lecture 6 Review  Described the algorithm for the data structure used in tree search  Moving on to uninformed search strategies When Does the Agent Test for a Goal State  Test-Before-Expansion ◦ Generates all children k and adds it into the fringe ◦ All children are generated before the goal is tested for ◦ Generate all children and then test for the goal state  Test-At-Expansion ◦ Generates only children that do not match the goal state up until the goal state is found ◦ Not all children are generated and added to the fringe ◦ Test for the goal state as the children are generated  Test-at-expansion is slightly better for time, but the difference is insignificant because if the goal state is the final state, there is NO difference between the two methods ◦ Most AI text books will use TBE ◦ For consistency, we will use TBE, not TAE  Usage of phrases relative to tree nodes ◦ Visit ▪ Perform goal test ▪ Expand the node if the goal test was unsuccessful ▪ Verb: visit ◦ Expansion ▪ Generate its children based on the successor function ▪ Verb: expand ◦ Generation ▪ Generation is NOT visitting ▪ Generation is creating a node without visitting it Measure Problem Solving Performance  Completeness: A search algorithm is complete if it guarantees to find a solution if one exists ◦ A tree that cannot have any more leaves added to the fringe is a fully expanded search tree ◦ Some fully expanded search trees are finite, others are infinite ◦ If you fully expand the search tree and somewhere in that tree there is a goal node, then the solution is considered to be existant ◦ If you fully expand the search tree and the goal node is not in the tree, then no solution exists ◦ An agent must be able to find the solution if it exists in order to be complete  Optimality: A search algorithm is optimal if it guarantees to find the optimal solution ◦ An optimal solution has the lowest path cost among all solutions ◦ Implies the agent is complete because an agent must be able to identify all the solutions in order to select the best one  Time Complexity: Time taken to find a solution ◦ Measured by max # of nodes generated ◦ In order to measure the time complexity of the search algorithm we analyze how many nodes you generate, doesn't matter how long the computation time is per node ▪ Doesn't matter if it varies to generate each individual node ◦ A numerical value  Space Complexity: Memory needed to perform search ◦ Measured by max number of nodes stored ▪ There are times when you don't have to store the entire search tree, and there are situations when you do have to store the entire search tree ◦ A numerical value  Efficiency of solution vs. Efficiency of search ◦ Completeness and optimality have to do with the quality of the search algorithm ◦ Time complexity and space complexity have to do with the efficiency of the search algorithm ◦ Time complexity discusses the efficiency of virtually solving the problem (How quickly can the agent think?) ◦ Optimality discusses the efficiency of physically solving the problem (How quickly can the agent act?) Measure Size of Problem  Why do we measure the size of a problem?  Size of a search problem can be measured by the folowing parameters of the fully expanded search tree.  Example of measuring the size of the problem:  Branching factor: b ◦ Max number of succ
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