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m4-heuristics

# m4-heuristics - Informed search algorithms Chapter 4...

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Informed search algorithms Chapter 4

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Material Chapter 4 Section 1 - 3 Exclude memory-bounded heuristic  search
Definition Uninformed search strategies Generate states Test them  With the goal Incredibly inefficient in  most cases Informed search strategies Problem-specific knowledge Evaluation Find solution more  efficiency Eval(State)  Eval(Goal)

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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
Review: Tree search A search strategy is defined by picking  the  order of node expansion

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Best-First search The knowledge is applied on the queue this is provided by an “ evaluation function” the node with the best evaluation is expanded first. Description Function Best_First_Search(Problem, Eval-Fn) return sol or fail Inputs: Problem, Evaluation_function; Queuing-Fn <- sort the queue by Eval-Fn return General_Search(Problem, Queuing-Fn)  Algorithm
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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Best-First search Function that calculates the cost to  reach a goal is called,  heuristic function denoted by  h : h(n)  = estimated cost of the cheapest path  from state at node n to the goal state
Romania with step costs in km

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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
Greedy best-first search  example

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Greedy best-first search  example
Greedy best-first search  example

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m4-heuristics - Informed search algorithms Chapter 4...

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