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Heuristic_Search_Part1

Heuristic_Search_Part1 - Heuristic(Informed Search R&N Chap...

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1 Heuristic (Informed) Search R&N: Chap. 4, Sect. 4.1–3
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2 Search Algorithm # 2 S EARCH# 2 1. I NS ERT (init ial-node ,FRI NGE) 2 . Re pe at: a. I f  e m pt y(FRI NGE)  the n r e tur n  f ailur e b . N REMO VE(FRI NGE) c . s S T AT E( N ) d. I f  GO AL?( s ) the n r e tur n  path or  goal s tate e . For  e ve r y s tate   s ’ in S UCCES S O RS ( s ) i. Cr e ate  a node   N’ as  a s uc c e s s or  of   N ii. I NS ERT ( N’ ,FRI NGE) Re c all that the  o rde ring o f FRING E de fine s  the   s e arc h s trate g y
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3 Best-First Search It exploits  state description  to estimate how “good”  each search node is An  evaluation function  f maps each node N of the  search tree to a real number  f(N)   0  [Traditionally, f(N) is an estimated cost; so, the smaller f(N), the more  promising N] Best-first search  sorts the FRINGE in increasing f   [Arbitrary order is assumed among nodes with equal f]
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4 Best-First Search It exploits  state description  to estimate how “good”  each search node is An  evaluation function  f maps each node N of the  search tree to a real number  f(N)   0  [Traditionally, f(N) is an estimated cost; so, the smaller f(N), the more  promising N] Best-first search  sorts the FRINGE in increasing f   [Random order is assumed among nodes with equal f] “Best” does not refer to the quality  of the generated path Best-first search does not generate  optimal paths in general  “Best” does not refer to the quality  of the generated path Best-first search does not generate  optimal paths in general 
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5 Romania with step costs in km
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6 Example
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7 Example
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8 Example
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9 Typically, f(N) estimates: either the  cost of a solution path through N Then f(N) = g(N) + h(N), where g(N) is the cost of the path from the initial node to N h(N) is an estimate of the cost of a path from N to a goal node or the  cost of a path from N to a goal node Then f(N) = h(N)           Greedy best-search But there are no limitations on f. Any function of your choice is  acceptable.  But will it help the search algorithm? How to construct f?
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10 Typically, f(N) estimates: either the  cost of a solution path through N Then f(N) = g(N) +  h(N) , where g(N) is the cost of the path from the initial node to N h(N) is an estimate of the cost of a path from N to a goal node or the  cost of a path from N to a goal node Then f(N) =  h(N) But there are no limitations on f. Any function of your choice is  acceptable.  But will it help the search algorithm?
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