Lecture-05-06-Heuristic_Search

# Lecture-05-06-Heuristic_Search - CS 561 Artificial...

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CS 561: Artificial Intelligence Instructor: Sofus A. Macskassy, [email protected] TA: Harris Chiu ( [email protected] ), Wed 2:45-4:45pm, PHE 328 Penny Pan ( [email protected] ), Fri 10am-noon, PHE 328 Lectures: MW 5:00-6:20pm, ZHS 159 Office hours: By appointment Class page: http://www-rcf.usc.edu/~macskass/CS561-Fall2010/ This class will use https://blackboard.usc.edu/webapps/login/ and class webpage - Up to date information - Lecture notes - Relevant dates, links, etc. Course material: [AIMA] Artificial Intelligence: A Modern Approach, by Stuart Russell and Peter Norvig. (3rd ed)

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Previously: Problem-Solving Problem solving: Goal formulation Problem formulation (states, operators) Search for solution Problem formulation: Initial state Operators Goal test Path cost Problem types: single state: accessible and deterministic environment multiple state: inaccessible and deterministic environment contingency: inaccessible and nondeterministic environment exploration: unknown state-space 2 CS561 - Lecture 05-06 - Macskassy - Fall 2010
Previously: Finding a solution Function General-Search( problem , strategy ) returns a solution , or failure initialize the search tree using the initial state problem loop do if there are no candidates for expansion then return failure choose a leaf node for expansion according to strategy if the node contains a goal state then return the corresponding solution else expand the node and add resulting nodes to the search tree end Solution: is a sequence of operators that bring you from current state to the goal state Basic idea: offline, systematic exploration of simulated state-space by generating successors of explored states (expanding) Strategy: The search strategy is determined by the order in which the nodes are expanded. 3 CS561 - Lecture 05-06 - Macskassy - Fall 2010

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Previously: Evaluation of search strategies A search strategy is defined by picking the order of node expansion. Search algorithms are commonly evaluated according to the following four criteria: Completeness: does it always find a solution if one exists? Time complexity: how long does it take as function of num. of nodes? Space complexity: how much memory does it require? Optimality: does it guarantee the least-cost solution? Time and space complexity are measured in terms of: b max branching factor of the search tree d depth of the least-cost solution m max depth of the search tree (may be infinity) 4 CS561 - Lecture 05-06 - Macskassy - Fall 2010
Previously: Uninformed search strategies Use only information available in the problem formulation Breadth-first Uniform-cost Depth-first Depth-limited Iterative deepening 5 CS561 - Lecture 05-06 - Macskassy - Fall 2010

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Now: heuristic search [AIMA Ch. 4] Informed search: Use heuristics to guide the search Best first A* Heuristics Hill-climbing Simulated annealing 6 CS561 - Lecture 05-06 - Macskassy - Fall 2010
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Lecture-05-06-Heuristic_Search - CS 561 Artificial...

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