'Greedy search' presentation slideshows

CSCE 580 Artificial Intelligence Ch.4: Informed (Heuristic) Search and Exploration

CSCE 580 Artificial Intelligence Ch.4: Informed (Heuristic) Search and Exploration. Fall 2011 Marco Valtorta mgv@cse.sc.edu. Acknowledgment. The slides are based on the textbook [AIMA] and other sources, including other fine textbooks and the accompanying slide sets

1.16k views • 85 slides

CSCE 580 Artificial Intelligence Ch.4: Informed (Heuristic) Search and Exploration

CSCE 580 Artificial Intelligence Ch.4: Informed (Heuristic) Search and Exploration. Fall 2008 Marco Valtorta mgv@cse.sc.edu. Acknowledgment. The slides are based on the textbook [AIMA] and other sources, including other fine textbooks and the accompanying slide sets

1.03k views • 85 slides

Artificial intelligence 1: informed search

1.25k views • 79 slides

Snakes - Active Contour

Snakes - Active Contour. By: Saar Arbel. Lecturer : Hagit Hel-Or. What is our objective?. To find a contour that best approximates the perimeter of an object. Subdividing or partitioning an image into its constituent regions or objects. But Wait.

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CS 4700: Foundations of Artificial Intelligence

CS 4700: Foundations of Artificial Intelligence. Prof. Bart Selman selman@cs.cornell.edu Machine Learning: Decision Trees R&N 18.3. Big Picture of Learning . Learning can be seen as fitting a function to the data. We can consider

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CS 2710, ISSP 2160

CS 2710, ISSP 2160. Chapter 3, Part 2 Heuristic Search. Heuristic Search. Take advantage of information about the problem. Best-First-Search. An evaluation function f determines order of nodes on the fringe (there are variations, depending on the search algorithm). Best-First-Search.

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Unsupervised Learning of Probabilistic Context-Free Grammar Using Iterative Biclustering

Unsupervised Learning of Probabilistic Context-Free Grammar Using Iterative Biclustering. Kewei Tu and Vasant Honavar Artificial Intelligence Research Laboratory Department of Computer Science Iowa State University www.cs.iastate.edu/~honavar/aigroup.html www.cild.iastate.edu.

670 views • 52 slides

Informed Search

Informed Search. Reading: Chapter 4.5. Agenda. Introduction of heuristic search Greedy search Examples: 8 puzzle, word puzzle A* search Algorithm, admissable heuristics, optimality Heuristics for other problems Homework. 8-puzzle URLS. http://www.permadi.com/java/puzzle8

654 views • 49 slides

Chapter 7 Propositional Satisfiability Techniques

Chapter 7 Propositional Satisfiability Techniques. Motivation. Propositional satisfiability: given a boolean formula e.g., (P ? Q) ? ( ? Q ? R ? S) ? ( ? R ? ? P), does there exist a model i.e., an assignment of truth values to the propositions that makes the formula true?

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Artificial Intelligence

Artificial Intelligence. Informed search algorithms Chapter 4, Sections 1-5. Outline. Informed = use problem-specific knowledge Which search strategies? Best-first search and its variants Heuristic functions? How to invent them Local search and optimization

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CMSC 671 Fall 2005

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Meta- Uczenie Maszynowe

Meta- Uczenie Maszynowe. W?odzis?aw Duch , Norbert Jankowski & Krzysztof Gr?bczewski Katedra Informatyki Stosowanej , Uniwersytet Miko?aja Kopernika , Toru? Google: W. Duch IPI PAN, 11 / 201 3. Center for Interdisciplinary Innovative Technologies.

525 views • 34 slides

Heuristic search

Heuristic search. Reading: AIMA 2 nd ed., Ch. 4.1-4.3. (Blind) Tree search so far. function TreeSearch ( problem , fringe ) n = InitialState ( problem ); fringe = Insert ( n ); while (1) If Empty ( fringe ) return failure; n = RemoveFront ( fringe );

370 views • 28 slides

CSC3203: AI for Games Informed search (1)

CSC3203: AI for Games Informed search (1). Patrick Olivier p.l.olivier@ncl.ac.uk. Depth-first. Iterative deepening. Breadth-first. Uninformed search: summary. Informed search strategies. Best-first search Greedy best-first search A* search Local beam search Simulated annealing search

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Problem Solving by Searching Search Methods : informed (Heuristic) search

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Semi-Local Affine Parts for Object Recognition

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Heuristic Search Methods

Heuristic Search Methods. Heuristic Functions Hill climbing Beam search Hill climbing (2) Greedy search. Methods that use a heuristic function to provide specific knowledge about the problem:. HEURISTIC FUNCTIONS: f : States --> Numbers f( T ) : expresses the quality of the state T

745 views • 20 slides

Scaling eCGA Model Building via Data-Intensive Computing

Abhishek Verma, Xavier Llora , Shivaram Venkataram , David E. Goldberg, Roy H. Campbell. Scaling eCGA Model Building via Data-Intensive Computing. Presenter: . Motivation. Genetic Algorithms ( GAs ) applied to very large scale data-intensive problems Current approach: MPI

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Local search algorithms

377 views • 15 slides

Classification of Search Problems

Classification of Search Problems. http://www.cis.temple.edu/~ingargio/cis587/readings/constraints.html. Optimization Problems . State Space Search. Constraint Satisfaction Problems. Search. Heuristic Search. Uninformed Search. Example: State Space Search. Figure.

379 views • 11 slides