4 heuristics search v2

8/9/2019 4 Heuristics Search V2

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Informed search algorithms

Chapter 4


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Material

Chapter 4 Section 1 3

Exclude memory-ounded heuristic

search


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!e"ie#$ %ree search

& search strategy is defined y pic'ing theorder of node expansion


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(est-first search

Idea$ use an e"aluation functionf(n) for each node estimate of )desiraility*

Implementation$

+rder the nodes in fringe in decreasing order of desiraility

Special cases$ greedy est-first search

&,search

Expand most desirale unexpanded node


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!omania #ith step costs in 'm


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.reedy est-first search

E"aluation function f(n) = h(n) /heuristic0

estimate of cost from nto goal

e2g2 hSLD(n) straight-line distance from nto (ucharest

.reedy est-first search expands the node

that appearsto e closest to goal


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.reedy est-first searchexample


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roperties of greedy est-firstsearch

Complete56o can get stuc' in loopse2g2 Iasi6eamtIasi6eamt

%ime5 +/m0 ut a good heuristic cangi"e dramatic impro"ement

Space5 +/m0 -- 'eeps all nodes inmemory

+ptimal5 6o


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&,search

Idea$ a"oid expanding paths that arealready expensi"e

E"aluation function f(n) = g(n) + h(n)

g(n) cost so far to reach n

h(n) estimated cost from nto goal

f(n) estimated total cost of path throughnto goal


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&,search example


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&,search example


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&,search example


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&,search example


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&,search example


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&,search example


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+ptimality of &,

&,expands nodes in order of increasing f"alue

.radually adds )f-contours) of nodes

Contour ihas all nodes #ith f=fi #here fi< fi+1


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;ocal search algorithms

In many optimi


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Example$ n-?ueens

ut n?ueens on an n noard #ith not#o ?ueens on the same ro# column ordiagonal


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@ill-climing search

);i'e climing E"erest in thic' fog #ithamnesia)


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@ill-climing search

rolem$ depending on initial state canget stuc' in local maxima


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@ill-climing search$ A-?ueens prolem

h numer of pairs of ?ueens that are attac'ing each other either directlyor indirectly

h = 17for the ao"e state


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@ill-climing search$ A-?ueens prolem

& local minimum #ith h = 1


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;ocal eam search

:eep trac' of kstates rather than Bust one

Start #ith ' randomly generated states

&t each iteration all the successors of all 'states are generated

If any one is a goal state stop= else selectthe ' est successors from the completelist and repeat2


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.enetic algorithms

& successor state is generated y comining t#o parentstates

Start #ith krandomly generated states /population0 & state is represented as a string o"er a finite alphaet

/often a string of s and 1s0 E"aluation function /fitness function02 @igher "alues foretter states2

roduce the next generation of states y selectioncrosso"er and mutation


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.enetic algorithms

>itness function$ numer of non-attac'ing pairs of ?ueens /min max A D FG GA0

G4F/G4HG3HGH110 31 G3F/G4HG3HGH110 GJ etc

4 heuristics search v2

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