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Informed search algorithms

Informed search algorithms

Informed search algorithms. Chapter 3. Outline. Best-first search Greedy best-first search A * search Heuristics CHAPTER 4 Local search algorithms Hill-climbing search Simulated annealing search Local beam search Genetic algorithms. Best-first search.

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541 views • 38 slides
CS 4100 Artificial Intelligence

CS 4100 Artificial Intelligence

CS 4100 Artificial Intelligence. Prof. C. Hafner Class Notes Feb 9, 2012. Informed search algorithms. Outline. Heuristics Best-first search Greedy best-first search A * search. A search strategy is defined by picking the order of node expansion. Best-first search.

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509 views • 25 slides
Informed Search II

Informed Search II

Informed Search II. CIS 391 Fall 2008. Outline. PART I Informed = use problem-specific knowledge Best-first search and its variants A * - Optimal Search using Knowledge Proof of Optimality of A * A * for maneuvering AI agents in games Heuristic functions? How to invent them PART II

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354 views • 25 slides
Discussion on Greedy Search and A*

Discussion on Greedy Search and A*

Discussion on Greedy Search and A*. Best-first search. Idea: use an evaluation function f(n) for each node estimate of "desirability" Expand most desirable unexpanded node Implementation : Order the nodes in fringe in decreasing order of desirability Special cases:

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409 views • 24 slides
Optimality of A*(standard proof)

Optimality of A*(standard proof)

Optimality of A*(standard proof). Suppose suboptimal goal G 2 in the queue. Let n be an unexpanded node on a shortest path to optimal goal G . f(G 2 ) = g(G 2 ) since h(G 2 )=0 > g(G) since G 2 is suboptimal >= f(n) since h is admissible

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298 views • 20 slides

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