'Uninformed search' diaporamas de présentation

Search

Search. Tamara Berg CS 590-133 Artificial Intelligence. Many slides throughout the course adapted from Dan Klein, Stuart Russell, Andrew Moore, Svetlana Lazebnik , Percy Liang, Luke Zettlemoyer. Course Information. Instructor: Tamara Berg ( tlberg@cs.unc.edu )

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Solving problems by searching

Solving problems by searching. Chapter 3. Why Search?. To achieve goals or to maximize our utility we need to predict what the result of our actions in the future will be. There are many sequences of actions, each with their own utility. We want to find, or search for, the best one.

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The next RP deliverable

The next RP deliverable. 20. Review: Generate and Test Paradigm. Propose possible solutions then test whether each proposal constitutes a solution Will illustrate with n-Queens problem. n-Queens Problem. n-Queens are to be placed on an n x n board

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Uninformed Search

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Administrative

Administrative. Project due Tuesday: Email your code to Yisheng Tang (yit311@lehigh.edu) no later than 11:59PM EST Monday February 6 (the day BEFORE the competition)

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Searching MIU

Searching MIU. Blind Searching. Graham Kendall. Problem Definition - 1. Initial State The initial state of the problem, defined in some suitable manner Operator A set of actions that moves the problem from one state to another. Problem Definition - 1. Neighbourhood (Successor Function)

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Chapter 2

King Saud University College of Computer and Information Sciences Information Technology Department IT422 - Intelligent systems. Chapter 2. Problem Solving by Searching (1). Objectives. Learn how to formulate a search problem.

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Review

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Lecture 3

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Last time: Problem-Solving

Last time: Problem-Solving. Problem solving: Goal formulation Problem formulation (states, operators) Search for solution Problem formulation: Initial state ? ? ? Problem types: single state: accessible and deterministic environment multiple state: ? contingency: ? exploration: ?.

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Last time: Problem-Solving

Last time: Problem-Solving. Problem solving: Goal formulation Problem formulation (states, operators) Search for solution Problem formulation: Initial state ? ? ? Problem types: single state: accessible and deterministic environment multiple state: ? contingency: ? exploration: ?.

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Uninformed Search Computer Science cpsc322, Lecture 5 (Textbook Chpt 3.5) Sept, 14, 2012

Uninformed Search Computer Science cpsc322, Lecture 5 (Textbook Chpt 3.5) Sept, 14, 2012. Recap. Search is a key computational mechanism in many AI agents We will study the basic principles of search on the simple deterministic planning agent model Generic search approach :

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Uninformed Search

515 views • 33 slides

Uninformed Search Introduction to Artificial Intelligence

Uninformed Search Introduction to Artificial Intelligence. Dr. Robin Burke. Review. Problem domain Static only our actions change the world Deterministic actions always work the way we expect Fully-observable we always know everything we need about the world To solve

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Heuristic Search

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Uninformed Search (cont.)

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CSCI 4310

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Artificial Intelligence

Artificial Intelligence. Informed Search Methods. Chapter 4. Review. Last week, we talked about: Uninformed search strategies Can find solutions to problems by generating new states Testing them against the goal.

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SEG 4560 Final Review

SEG 4560 Final Review. Final. 4:15-6:15pm, Friday April 24, 2009 6 Questions from Chapters 3-15a Two A4 size cheat sheets A calculator Useful materials: Tutorials 1-3, Assignments 1-3, Midterm, and Lecture notes. Chapter 15a. Markov processes Sensor model Transition model

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Practical 1: Representation and Uninformed Search

Practical 1: Representation and Uninformed Search. Exercise 1: Search Visit the uninformed search online demo page: http://www.cse.unl.edu/~choueiry/S03-476-876/searchapplet/

164 views • 4 slides