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mychapter4 - Informed search algorithms CHAPTER 4 Oliver...

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CHAPTER 4 Oliver Schulte Summer 2011 Informed search algorithms

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Outline Best-first search A *  search Heuristics Local search algorithms Hill-climbing search Simulated annealing search Local beam search
Review: Tree search A search strategy is defined by picking the  order of  node expansion Which nodes to check first?

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Knowledge and Heuristics Simon and Newell,  Human Problem Solving , 1972. Thinking out loud: experts have strong opinions like  “this looks promising”, “no way this is going to  work”. heuristics  that help  find promising states fast.
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 frontier in decreasing order of  desirability Special cases: greedy best-first search A *  search

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Romania with step costs in km
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
Greedy best-first search example

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Greedy best-first search example
Greedy best-first search example http://aispace.org/search/

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Properties of greedy best-first search Complete?  No – can get stuck in loops,  e.g. as Oradea as goal  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
A *  search Idea: avoid expanding paths that are already  expensive. Very important!

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This note was uploaded on 07/04/2011 for the course CMPT 310 taught by Professor Oliver during the Summer '11 term at Simon Fraser.

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mychapter4 - Informed search algorithms CHAPTER 4 Oliver...

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