6 Informed searching

6 Informed searching - Informed search algorithms Outline...

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Informed search algorithms
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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
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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: greedy best-first search A * search
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Greedy best-first search Evaluation function f(n) = h(n) ( h euristic) = estimate of cost from n to goal • e.g., h SLD (n) = straight-line distance from n to Bucharest Greedy best-first search expands the node that appears to be closest to goal
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Greedy best-first search example
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Greedy best-first search example
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Greedy best-first search example
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Greedy best-first search example
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Properties of greedy best-first search Complete? No – can get stuck in loops, e.g., Iasi Neamt Iasi Neamt Time? O(b m ) , but a good heuristic can give dramatic improvement Space? O(b m ) -- keeps all nodes in memory Optimal? No
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* search Idea: avoid expanding paths that are already expensive Evaluation function f(n) = g(n) + h(n) g(n)
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This note was uploaded on 04/05/2010 for the course CS 723 taught by Professor Sc during the Spring '10 term at Jaypee University IT.

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6 Informed searching - Informed search algorithms Outline...

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