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State space representations and search strategies - 2

State space representations and search strategies - 2

State space representations and search strategies - 2. Spring 2007, Juris V ?ksna. Search strategies - A*. <S,P,I,G,W> - state space h*(x) - a minimum path weight from x to the goal state h(x) - heuristic estimate of h*(x) g*(x) - a minimum path weight from I to x

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752 views • 72 slides
Course Outline 4.2 Searching with Problem-specific Knowledge

Course Outline 4.2 Searching with Problem-specific Knowledge

Course Outline 4.2 Searching with Problem-specific Knowledge. Presented by: Wing Hang Cheung Paul Anh Tri Huynh Mei-Ki Maggie Yang Lu Ye CPSC 533 January 25, 2000. Chapter 4 - Informed Search Methods. 4.1 Best-First Search 4.2 Heuristic Functions 4.3 Memory Bounded Search

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624 views • 62 slides
Informed Search Algorithms

Informed Search Algorithms

Informed Search Algorithms. Chapter 4. Outline. Best-first search Greedy best-first search A * search Heuristics. Review: Tree search. Basic idea: offline, simulated exploration of state space by generating successors of already-explored states ( a.k.a.~ expanding states)

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481 views • 30 slides
Informed (Heuristic) Search Algorithms

Informed (Heuristic) Search Algorithms

Informed (Heuristic) Search Algorithms. Homework #1 assigned due 10/4 before Exam 1. 9. 7. 7. A. .1. N1:B(.1+25.2). N1:B(.1+8.6). N2:G(9+0). N2:G(9+0). 25.2. 8.6. 20. B. .1. 9. N3:C(.2+8.7). 25.1. 8.7. 28. C. .1. 25. 25. 25. D. N4:D(.3+25). 25. 0. 0. 0. G.

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361 views • 27 slides
Using Pattern Recognition Techniques to Derive a Formal Analysis of Why Heuristic Functions Work

Using Pattern Recognition Techniques to Derive a Formal Analysis of Why Heuristic Functions Work

B. John Oommen A Joint Work with Luis G. Rueda School of Computer Science Carleton University. Using Pattern Recognition Techniques to Derive a Formal Analysis of Why Heuristic Functions Work. Optimization Problems. Any arbitrary optimization problem: Instances, drawn from a finite set, X,

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344 views • 21 slides

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