'Local beam search' diaporamas de présentation

Informed Search and Exploration

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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

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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

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

867 views • 85 slides

CS 4700: Foundations of Artificial Intelligence

CS 4700: Foundations of Artificial Intelligence. Bart Selman selman@cs.cornell.edu Local Search Readings R&N: Chapter 4:1 and 6:4. So far: methods that systematically explore the search space, possibly using principled pruning (e.g., A*)

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Informed Search

CSC 463 Fall 2010. Informed Search. Sections 3.5, 3.6, 4.1, 4.5 Classes 5 and 6. Adapted from materials by Marie desJardins, Tim Finin , Eric Eaton, and Charles R. Dyer. Outline. Heuristic search Best-first search Greedy search Beam search A, A* Examples

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Beyond Classical Search (Local Search) R&N III: Chapter 4

Beyond Classical Search (Local Search) R&N III: Chapter 4. Local Search. Light-memory search method No search tree; only the current state is represented! Only applicable to problems where the path is irrelevant (e.g., 8-queen), unless the path is encoded in the state

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CS 2710, ISSP 2610

CS 2710, ISSP 2610. Chapter 4, Part 2 Heuristic Search. Beam Search. Cheap, unpredictable search For problems with many solutions, it may be worthwhile to discard unpromising paths Greedy best first search that keeps a fixed number of nodes on the fringe. Beam Search.

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Informed search algorithms

Informed search algorithms. Chapter 4. Outline. Best-first search Greedy best-first search A * search Heuristics Local search algorithms Hill-climbing search Simulated annealing search Local beam search Genetic algorithms. Review: Tree search.

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Informed Search Chapter 4 (b)

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CS 2710, ISSP 2610

CS 2710, ISSP 2610. R&N Chapter 4.1 Local Search and Optimization. Search Types. Backtracking state-space search Local Search and Optimization Constraint satisfaction search Adversarial search. Local Search and Optimization.

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Informed Search Chapter 4 (b)

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Informed Search Chapter 4 (b)

408 views • 27 slides

Informed Search Chapter 4 (b)

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For Monday

For Monday. Chapter 6 Homework: Chapter 3, exercise 7. Lisp questions?. Program 1. Late Tickets. You have 2 for the semester. Only good for programs. Allow you to hand in up to 5 days late IF you have a late ticket left. Each good for +.05 on final grade if unused.

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I terative Improvement Algorithm

I terative Improvement Algorithm. 2012/03/20. Outline. Local Search Algorithms Hill-Climbing Search Simulated Annealing Search Local Beam Search Genetic Algorithms. I terative Improvement Algorithm. General idea of local search start with a complete configuration

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Informed Search Chapter 4 (b)

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Informed Search Chapter 4 (b)

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Local Search Algorithms

Local Search Algorithms. CPS 4801. Outline. Hill-Climbing Search Simulated Annealing Local Beam Search (briefly ). Local search algorithms. In many optimization problems, the path to the goal is irrelevant; the goal state itself is the solution.

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Informed Search and Exploration

Informed Search and Exploration. Search Strategies Heuristic Functions Local Search Algorithms. Introduction. Heuristic search strategies: Use problem specific knowledge Can find solutions more efficiently than search strategies that do not use domain specific knowledge.

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