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پاورپوینت در مورد Artificial intelligence 1 informed search

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پاورپوینت در مورد Artificial intelligence 1 informed search

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Artificial intelligence 1: informed search
v
2
07 September 2019
Outline
Informed = use problem-specific knowledge
Which search strategies?
Best-first search and its variants
Heuristic functions?
How to invent them
Local search and optimization
Hill climbing, local beam search, genetic algorithms,…
Local search in continuous spaces
Online search agents
TLo (IRIDIA)
3
07 September 2019
Previously: tree-search
function TREE-SEARCH( problem,fringe ) return a solution or failure
fringe  INSERT( MAKE-NODE(INITIAL-STATE[ problem ]), fringe )
loop do
if EMPTY?( fringe ) then return failure
node  REMOVE-FIRST( fringe )
if GOAL-TEST[ problem ] applied to STATE[ node ] succeeds
then return SOLUTION( node )
fringe  INSERT-ALL(EXPAND( node , problem ), fringe )
A strategy is defined by picking the order of node expansion
TLo (IRIDIA)
4
07 September 2019
Best-first search
General approach of informed search:
Best-first search: node is selected for expansion based on an evaluation function f(n)
Idea: evaluation function measures distance to the goal.
Choose node which appears best
Implementation:
fringe is queue sorted in decreasing order of desirability.
Special cases: greedy search, A* search

 

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