We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!
Presentation is loading. Please wait.
Published byJailyn Bolt
Modified about 1 year ago
Strategi Pencarian dengan Informasi (Informed Search Strategy)
Outline Best-first search Greedy best-first search A * search Heuristics Local search algorithms Hill-climbing search Local beam search Genetic algorithms
Greedy best-first search example
A * search example
Stategi Pencarian Lokal (Local Search)
Hill-climbing search Local search
Hill-climbing search Problem: depending on initial state, can get stuck in local maxima
Hill-climbing search: 8-queens problem h = number of pairs of queens that are attacking each other, either directly or indirectly h = 17 for the above state
Hill-climbing search: 8-queens problem A local minimum with h = 1
END OF SLIDE
STRATEGI PENCARIAN DENGAN INFORMASI (INFORMED SEARCH STRATEGY)
Local search algorithms In many optimization problems, the path to the goal is irrelevant; the goal state itself is the solution State space = set of "complete"
Local Search Algorithms CPS Outline Hill-Climbing Search Simulated Annealing Local Beam Search (briefly)
Informed search algorithms Chapter 4. Material Chapter 4 Section Exclude memory-bounded heuristic search.
1 Informed Search CS 171/271 (Chapter 4) Some text and images in these slides were drawn from Russel & Norvigs published material.
Chapter 4. Local search algorithms ◦ Hill-climbing search ◦ Simulated annealing search ◦ Local beam search Genetic algorithms.
Constraints Satisfaction Edmondo Trentin, DIISM. Constraint Satisfaction Problems: Local Search In many optimization problems, the path to the goal is.
Local Search Algorithms and Optimization Problems Section 4.1.
CHAPTER 4, Part II Oliver Schulte Summer 2011 Local Search.
Local Search Algorithms Chapter 4. Outline Hill-climbing search Simulated annealing search Local beam search Genetic algorithms Ant Colony Optimization.
1 Shanghai Jiao Tong University Informed Search and Exploration.
CPSC 420 – Artificial Intelligence Texas A & M University Lecture 5 Lecturer: Laurie webster II, M.S.S.E., M.S.E.e., M.S.BME, Ph.D., P.E.
Informed search algorithms Chapter 4. Best-first search Idea: use an evaluation function f(n) for each node –estimate of "desirability" Expand most.
Feng Zhiyong Tianjin University Fall Best-first search Greedy best-first search A * search Heuristics Local search algorithms Hill-climbing.
Local Search Pat Riddle 2012 Semester 2 Patricia J Riddle Adapted from slides by Stuart Russell,
Local Search and Stochastic Algorithms Solution tutorial 4.
Local Search Algorithms CMPT 463. When: Tuesday, April 5 3:30PM Where: RLC 105 Team based: one, two or three people per team Languages: Python, C++ and.
Best-first search Idea: use an evaluation function f(n) for each node –estimate of "desirability" Expand most desirable unexpanded node Implementation:
Informed search algorithms Chapter 4. Outline Best-first search Greedy best-first search A * search Heuristics Local search algorithms Hill-climbing search.
4/11/2005EE562 EE562 ARTIFICIAL INTELLIGENCE FOR ENGINEERS Lecture 4, 4/11/2005 University of Washington, Department of Electrical Engineering Spring 2005.
An Introduction to Artificial Life Lecture 4b: Informed Search and Exploration Ramin Halavati In which we see how information.
1 Informed search algorithms Chapter 4 Thanks: Prof Dan Weld, Univ of Washington, Seattle USA.
Search CSE When you can’t use A* Hill-climbing Simulated Annealing Other strategies 2 person- games.
Princess Nora University Artificial Intelligence Chapter (4) Informed search algorithms 1.
AI CSC361: Problem Solving & Search 1 Problem Solving by Searching CSC361.
CS 460 Spring 2011 Lecture 3 Heuristic Search / Local Search.
INTRODUÇÃO AOS SISTEMAS INTELIGENTES Prof. Dr. Celso A.A. Kaestner PPGEE-CP / UTFPR Agosto de 2011.
CPSC 322, Lecture 14Slide 1 Local Search Computer Science cpsc322, Lecture 14 (Textbook Chpt 4.8) February, 4, 2009.
Adapted by Doug Downey from Bryan Pardo Fall 2007 Machine Learning EECS 349 Machine Learning Lecture 4: Greedy Local Search (Hill Climbing)
Announcement "A note taker is being recruited for this class. No extra time outside of class is required. If you take clear, well-organized notes, this.
Local Search and Optimization 22c:31:002 Algorithms.
For Wednesday Read chapter 5, sections 1-4 Homework: –Chapter 3, exercise 23. Then do the exercise again, but use greedy heuristic search instead of A*
CPSC 322, Lecture 14Slide 1 Local Search Computer Science cpsc322, Lecture 14 (Textbook Chpt 4.8) February, 3, 2010.
CPSC 322, Lecture 14Slide 1 Local Search Computer Science cpsc322, Lecture 14 (Textbook Chpt 4.8) Oct, 5, 2012.
For Wednesday Read chapter 6, sections 1-3 Homework: –Chapter 4, exercise 1.
LOCAL SEARCH AND CONTINUOUS SEARCH. Local search algorithms In many optimization problems, the path to the goal is irrelevant ; the goal state itself.
Slide 1 Local Search Jim Little UBC CS 322 – CSP October 3, 2014 Textbook §4.8.
CS482/682 Artificial Intelligence Lecture 7: Genetic Algorithms and Constraint Satisfaction Problems 15 September 2009 Instructor: Kostas Bekris Computer.
CHAPTER 4 Informed search algorithms 1. Outline Best-first search Greedy best-first search A * search Heuristics Local search algorithms Hill-climbing.
Eight queens puzzle. The eight queens puzzle is the problem of placing eight chess queens on an 8×8 chessboard such that none of them are able to capture.
CPSC 322, Lecture 15Slide 1 Stochastic Local Search Computer Science cpsc322, Lecture 15 (Textbook Chpt 4.8) February, 6, 2009.
CSC344: AI for Games Lecture 4: Informed search Patrick Olivier
A General Introduction to Artificial Intelligence.
Local Search Algorithms This lecture topic Chapter Next lecture topic Chapter 5 (Please read lecture topic material before and after each lecture.
Local Search and Stochastic Algorithms Tutorial. Hill Climbing Describe the way to use Hill Climbing to solve N-Queens Problem.
Iterative Improvement Algorithm 2012/03/20. Outline Local Search Algorithms Hill-Climbing Search Simulated Annealing Search Local Beam Search Genetic.
Introduction to Artificial Intelligence Local Search (updated 4/30/2006) Henry Kautz.
© 2017 SlidePlayer.com Inc. All rights reserved.