AI ch4

‫آﮔﺎﻫﺎﻧﻪ‬ ‫ﺟﺴﺘﺠﻮي‬ ‫ﻫﺎي‬ ‫روش‬‫ﺠﻮي‬ ‫ﺟ‬ ‫ي‬ ‫روش‬
‫ﭼﻬﺎرم‬ ‫ﻓﺼﻞ‬
‫رﺿﻮي‬ ‫ﻧﺎﺻ‬ ‫ﺪ‬ ‫ﺳ‬‫رﺿﻮي‬ ‫ﻧﺎﺻﺮ‬ ‫ﺳﻴﺪ‬
razavi@Comp.iust.ac.irEmail:
1386
‫ﻣﻘﺪﻣﻪ‬‫ﻣﻘﺪﻣﻪ‬
•‫ﺟﺴﺘﺠﻮي‬‫اول‬-‫ﺑﻬﺘﺮﻳﻦ‬(Best-First Search) ‫ﺟﺴﺘﺠﻮي‬‫اول‬‫ﺑﻬﺘﺮﻳﻦ‬(Best First Search)
•‫ﺟﺴﺘﺠﻮي‬‫ﺣﺮﻳﺼﺎﻧﻪ‬(Greedy search)
•‫ﺟﺴﺘﺠﻮي‬A*‫و‬‫ﺧﺼﻮﺻﻴﺎت‬‫آن‬ ‫ﺠﻮي‬ ‫ﺟ‬‫و‬‫ت‬ ‫ﺼﻮﺻﻴ‬‫ن‬
•‫ﺟﺴﺘﺠﻮي‬‫ﻋﻤﻴﻖ‬‫ﻛﻨﻨﺪه‬‫ﺗﻜﺮاري‬A*‫و‬SMA*
•‫ﺘﺠﻮي‬ ‫ﺟ‬‫اول‬‫ﻳﻦ‬ ‫ﺑﻬﺘ‬‫ﺑﺎزﮔﺸﺘ‬(RBFA*) ‫ﺟﺴﺘﺠﻮي‬‫اول‬‫ﺑﻬﺘﺮﻳﻦ‬‫ﺑﺎزﮔﺸﺘﻲ‬(RBFA )
•‫ﻫﻴﻮرﻳﺴﺘﻴﻚ‬‫ﻫﺎ‬(Heuristics)
‫اﻟﮕ‬‫ﺎ‬‫ﻠ‬ •‫اﻟﮕﻮرﻳﺘﻢ‬‫ﻫﺎي‬‫ﺟﺴﺘﺠﻮي‬‫ﻣﺤﻠﻲ‬
•‫ﺟﺴﺘﺠﻮي‬‫ﺗﭙﻪ‬‫ﻧﻮردي‬(Hill Climbing)
Si l t d A li •‫ﺟﺴﺘﺠﻮي‬Simulated Annealing
•‫اﻟﮕﻮرﻳﺘﻢ‬‫ﻫﺎي‬‫ژﻧﺘﻴﻚ‬
N. Razavi - AI course - 2007 2
‫ﻧﺎآﮔﺎﻫﺎﻧﻪ‬ ‫ﺟﺴﺘﺠﻮي‬ ‫ﻫﺎي‬ ‫روش‬ ‫ﻣﺸﻜﻼت‬‫ﻧﺎآﮔﺎﻫﺎﻧﻪ‬ ‫ﺟﺴﺘﺠﻮي‬ ‫ﻫﺎي‬ ‫روش‬ ‫ﻣﺸﻜﻼت‬
‫ﮔ‬‫ﮔ‬‫ﻄ‬‫آ‬ •‫ﻣﻌﻴﺎر‬‫اﻧﺘﺨﺎب‬‫ﮔﺮه‬‫ﺑﻌﺪي‬‫ﺑﺮاي‬‫ﮔﺴﺘﺮش‬‫دادن‬‫ﺗﻨﻬﺎ‬‫ﺑﻪ‬‫ﺷﻤﺎره‬‫ﺳﻄﺢ‬‫آن‬
‫ﺑﺴﺘﮕﻲ‬‫دارد‬. ‫ﻲ‬ ‫ﺑ‬‫ر‬
•‫از‬‫ﺳﺎﺧﺘﺎر‬‫ﻣﺴﺄﻟﻪ‬‫ﺑﻬﺮه‬‫ﻧﻤﻲ‬‫ﺑﺮﻧﺪ‬.
•‫درﺧﺖ‬‫ﺟﺴﺘﺠﻮ‬‫را‬‫ﺑﻪ‬‫ﻳﻚ‬‫روش‬‫از‬‫ﭘﻴﺶ‬‫ﺗﻌﺮﻳﻒ‬‫ﺷﺪه‬‫ﮔﺴﺘﺮش‬
‫ﻣ‬‫دﻫﻨﺪ‬‫ﻳﻌﻨ‬‫ﺖ‬ ‫ﻗﺎﺑﻠ‬‫ﻖ‬ ‫ﺗﻄ‬‫ي‬ ‫ﭘﺬﻳ‬‫ﺑﺎ‬‫آﻧﭽﻪ‬‫ﻛﻪ‬‫ﺗﺎ‬‫ن‬ ‫ﻛﻨ‬‫د‬‫ﻣ‬ ‫ﻣﻲ‬‫دﻫﻨﺪ‬.‫ﻳﻌﻨﻲ‬‫ﻗﺎﺑﻠﻴﺖ‬‫ﺗﻄﺒﻴﻖ‬‫ﭘﺬﻳﺮي‬‫ﺑﺎ‬‫آﻧﭽﻪ‬‫ﻛﻪ‬‫ﺗﺎ‬‫ﻛﻨﻮن‬‫در‬‫ﻣﺴﻴﺮ‬
‫ﺟﺴﺘﺠﻮ‬‫درﻳﺎﻓﺘﻪ‬‫اﻧﺪ‬‫و‬‫ﻧﻴﺰ‬‫ﺣﺮﻛﺘﻲ‬‫ﻛﻪ‬‫ﻣﻲ‬‫ﺗﻮاﻧﺪ‬‫ﺧﻮب‬‫ﺑﺎﺷﺪ‬‫ﻧﺪارﻧﺪ‬.
‫ﻣﺮور‬‫درﺧﺖ‬ ‫ﺟﺴﺘﺠﻮي‬ ‫ﻣﺮور‬:‫درﺧﺖ‬ ‫ﺟﺴﺘﺠﻮي‬
function GENERAL-SEARCH( problem, strategy) returns a solution , or failure
initialize the search tree using the initial state of probleminitialize the search tree using the initial state of problem
loop do
if there are no candidates for expansion then return failure
choose a leaf node for expansion according to strategy
if the node contains the goal state then return the corresponding solution
else expand the node and add the resulting node to the search treeelse expand the node and add the resulting node to the search tree
end
••‫اﺳﺘﺮاﺗﮋي‬‫ﺗﻮﺳﻂ‬‫ﺗﺮﺗﻴﺐ‬‫ﺗﺮﺗﻴﺐ‬‫ﮔﺴﺘﺮش‬‫ﮔﺴﺘﺮش‬‫ﻳﺎﻓﺘﻦ‬‫ﻳﺎﻓﺘﻦ‬‫ﮔﺮه‬‫ﮔﺮه‬‫ﻫﺎ‬‫ﻫﺎ‬‫ﺗﻌﺮﻳﻒ‬‫ﻣﻲ‬‫ﺷﻮد‬.

‫اول‬ ‫ﺟﺴﺘﺠﻮي‬‫ﺑﻬﺘﺮﻳﻦ‬ ‫اول‬ ‫ﺟﺴﺘﺠﻮي‬-‫ﺑﻬﺘﺮﻳﻦ‬
•‫اﻳﺪه‬:‫ﺑﺮاي‬‫ﻫﺮ‬‫ﮔﺮه‬‫از‬‫ﻳﻚ‬‫ﺗﺎﺑﻊ‬‫ﺗﺎﺑﻊ‬‫ارزﻳﺎﺑﻲ‬‫ارزﻳﺎﺑﻲ‬(evaluation function)‫اﺳﺘﻔﺎده‬‫ﻛﻦ‬.
–‫ﺗﺨﻤﻴﻦ‬”‫ﻣﻴﺰان‬‫ﻣﻄﻠﻮب‬‫ﺑﻮدن‬“‫ﮔﺮه‬
‫ﻫﺮﺑﺎر‬‫ﻣﻄﻠﻮب‬‫ﺗﺮﻳﻦ‬‫ﮔﺮه‬‫ﮔﺴﺘﺮش‬‫ﻧﻴﺎﻓﺘﻪ‬‫را‬‫ﺑﺴﻂ‬‫ﻣﻲ‬‫دﻫﺪ‬.
••‫ﭘﻴﺎده‬‫ﭘﻴﺎده‬‫ﺳﺎزي‬‫ﺳﺎزي‬::
fringefringe‫ﻳﻚ‬‫ﺻﻒ‬‫ﻣﻲ‬‫ﺑﺎﺷﺪ‬‫ﻛﻪ‬‫ﺑﺮ‬‫اﺳﺎس‬‫ﻣﻴﺰان‬‫ﻣﻄﻠﻮب‬‫ﺑﻮدن‬‫ﮔﺮه‬‫ﻫﺎ‬‫ﻣﺮﺗﺐ‬‫ﻣﻲ‬‫ﺑﺎﺷﺪ‬. ‫ﻳ‬‫ﻲ‬‫ﺑ‬‫ﺑﺮ‬‫س‬‫ﻴﺰ‬‫ﻮب‬‫ﺑﻮ‬‫ﺮ‬‫ﺐ‬ ‫ﺮ‬‫ﻲ‬‫ﺑ‬
•‫ﻣﻮارد‬‫ﺧﺎص‬: ‫ر‬ ‫ﻮ‬‫ص‬
–‫ﺟﺴﺘﺠﻮي‬‫ﺣﺮﻳﺼﺎﻧﻪ‬(Greedy search)
–‫ﺟﺴﺘﺠﻮي‬A*
‫ﻞ‬ ‫ا‬ ‫ﻨ‬ ‫ﺰ‬ ‫ا‬ ‫ﺎﻧ‬ ‫ﻧﻘﺸ‬km ‫ﺑﺮﺣﺴﺐ‬ ‫ﻣﺮاﺣﻞ‬ ‫ﻫﺰﻳﻨﻪ‬ ‫ﻫﻤﺮاه‬ ‫ﺑﻪ‬ ‫روﻣﺎﻧﻲ‬ ‫ﻧﻘﺸﻪ‬km
‫اول‬ ‫ﺟﺴﺘﺠﻮي‬‫ﺣﺮﻳﺼﺎﻧﻪ‬ ‫ﺑﻬﺘﺮﻳﻦ‬ ‫اول‬ ‫ﺟﺴﺘﺠﻮي‬-‫ﺣﺮﻳﺼﺎﻧﻪ‬ ‫ﺑﻬﺘﺮﻳﻦ‬
•‫ﺗﺎﺑﻊ‬‫ارزﻳﺎﺑﻲ‬f(n) = h( n))‫ﻫﻴﻮرﻳﺴﺘﻴﻚ‬(
‫ﮔ‬‫ﻜ‬ •=‫ﻫﺰﻳﻨﻪ‬‫ﺗﺨﻤﻴﻨﻲ‬‫از‬‫ﮔﺮه‬n‫ﺗﺎ‬‫ﻧﺰدﻳﻜﺘﺮﻳﻦ‬‫ﻫﺪف‬
•‫ﻣﺜﺎل‬:hSLD(n)=‫ﻓﺎﺻﻠﻪ‬‫ﻣﺴﺘﻘﻴﻢ‬‫از‬n‫ﺗﺎ‬‫ﺑﺨﺎرﺳﺖ‬. SLD( )‫ﻴﻢ‬‫ﺑ‬
‫ﮔ‬‫ﮔ‬ •‫ﺟﺴﺘﺠﻮي‬‫ﺣﺮﻳﺼﺎﻧﻪ‬‫ﮔﺮه‬‫اي‬‫را‬‫ﮔﺴﺘﺮش‬‫ﻣﻲ‬‫دﻫﺪ‬‫ﻛﻪ‬‫ﺑﻪ‬‫ﻧﻈﺮ‬‫ﻣﻲ‬‫رﺳﺪ‬
‫ﻳﻦ‬ ‫ﻧﺰدﻳﻜﺘ‬‫ه‬ ‫ﮔ‬‫ﺑﻪ‬‫ﻫﺪف‬)‫ﺑﺨﺎرﺳﺖ‬(‫ﺑﺎﺷﺪ‬ ‫ﻧﺰدﻳﻜﺘﺮﻳﻦ‬‫ﮔﺮه‬‫ﺑﻪ‬‫ﻫﺪف‬)‫ﺑﺨﺎرﺳﺖ‬(‫ﺑﺎﺷﺪ‬.
‫ﺣﺮﻳﺼﺎﻧﻪ‬ ‫ﺟﺴﺘﺠﻮي‬ ‫ﻣﺜﺎل‬‫ﺣﺮﻳﺼﺎﻧﻪ‬ ‫ﺟﺴﺘﺠﻮي‬ ‫ﻣﺜﺎل‬
Arad
ZerindTimisoaraSibiu
A d F O d
Rimnicu
253 374329
Arad Fagaras Oradea
Rimnicu
Vilcea
366 380176 193
0: [A(366)]
1: [S(253), T(329), Z(374)]
2 [F(176) R(193) T(329) A(366)
Sibiu Bucharest
0
2: [F(176), R(193), T(329), A(366),
Z(374), O(380)]
3: [B(0), R(193), S(253), T(329), A(366),
0253
[ ( ), ( ), ( ), ( ), ( ),
Z(374), O(380)]
4: [R(193), S(253), T(329), A(366),
Z(374) O(380)]
Z(374), O(380)]

‫ﺣﺮﻳﺼﺎﻧﻪ‬ ‫ﺟﺴﺘﺠﻮي‬ ‫ﺧﻮاص‬‫ﺣﺮﻳﺼﺎﻧﻪ‬ ‫ﺟﺴﺘﺠﻮي‬ ‫ﺧﻮاص‬
•‫ﻛﺎﻣﻞ‬‫؟‬‫؟‬،‫ﺧﻴﺮ‬‫ﻣﻤﻜﻦ‬‫اﺳﺖ‬‫در‬‫ﺣﻠﻘﻪ‬‫ﮔﻴﺮ‬‫ﻛﻨﺪ‬.
–‫ﻣﺜﺎل‬:‫ﺣﺎﻟﺖ‬‫اوﻟﻴﻪ‬Iasi،‫ﺣﺎﻟﺖ‬‫ﻫﺪف‬Fagaras ‫ل‬‫ﻴ‬ ‫و‬‫ف‬g
Iasi Neamt Iasi Neamt …
•‫ﭘﻴﭽﻴﺪﮔﻲ‬‫زﻣﺎﻧﻲ‬‫؟‬‫؟‬O(bm)،‫اﻣﺎ‬‫ﺑﺎ‬‫ﻳﻚ‬‫ﻫﻴﻮرﻳﺴﺘﻴﻚ‬‫ﺧﻮب‬‫ﻣﻲ‬‫ﺗﻮاﻧﺪ‬‫ﺑﺴﻴﺎر‬‫ﺑﻬﺒﻮد‬‫ﻳﺎﺑﺪ‬.
•‫ﭘﻴﭽﻴﺪﮔﻲ‬‫ﺣﺎﻓﻈﻪ‬‫؟‬‫؟‬O(bm)،‫ﺗﻤﺎم‬‫ﮔﺮه‬‫ﻫﺎ‬‫را‬‫در‬‫ﺣﺎﻓﻈﻪ‬‫ﻧﮕﻪ‬‫ﻣﻲ‬‫دارد‬. ‫م‬
•‫ﺑﻬﻴﻨﻪ؟‬،‫ﺧﻴﺮ‬)‫ﺑﺎ‬‫ﺗﻮﺟﻪ‬‫ﺑﻪ‬‫ﻣﺜﺎل‬‫ﻗﺒﻞ‬( ‫؟‬ ‫ﺑﻬﻴ‬،‫ﻴﺮ‬)‫ﺑ‬‫ﻮﺟ‬‫ﺑ‬‫ل‬‫ﺒﻞ‬(
‫ﺟﺴﺘﺠﻮي‬A* ‫ﺟﺴﺘﺠﻮي‬A*
•‫اﻳﺪه‬:‫از‬‫ﮔﺴﺘﺮش‬‫ﻣﺴﻴﺮﻫﺎﻳﻲ‬‫ﻛﻪ‬‫ﺗﺎﻛﻨﻮن‬‫ﻣﺸﺨﺺ‬‫ﺷﺪه‬‫ﭘﺮﻫﺰﻳﻨﻪ‬‫ﻣﻲ‬،‫ﺑﺎﺷﻨﺪ‬‫اﺟﺘﻨﺎب‬‫ﻛﻦ‬.
•A*:‫ﺗﺮﻛﻴﺐ‬‫ﻣﺰاﻳﺎي‬UCSUCS‫و‬‫ﺟﺴﺘﺠﻮي‬‫ﺟﺴﺘﺠﻮي‬‫ﺣﺮﻳﺼﺎﻧﻪ‬‫ﺣﺮﻳﺼﺎﻧﻪ‬:
–‫ﺟﺴﺘﺠﻮي‬‫ﺣﺮﻳﺼﺎﻧﻪ‬h(n)‫را‬‫ﺣﺪاﻗﻞ‬‫ﻣﻲ‬،‫ﻛﻨﺪ‬‫ﻧﻪ‬‫ﻛﺎﻣﻞ‬‫و‬‫ﻧﻪ‬‫ﺑﻬﻴﻨﻪ‬ ‫ﺠﻮي‬ ‫ﺟ‬‫ﺮﻳ‬( )‫ر‬‫ﻞ‬‫ﻲ‬‫ﻞ‬‫و‬‫ﺑﻬﻴ‬
–‫ﺟﺴﺘﺠﻮي‬UCS،‫ﻫﺰﻳﻨﻪ‬‫ﻣﺴﻴﺮ‬‫را‬‫ﺣﺪاﻗﻞ‬‫ﻣﻲ‬‫ﻛﻨﺪ؛‬‫ﻛﺎﻣﻞ‬‫و‬‫ﺑﻬﻴﻨﻪ‬‫اﺳﺖ؛‬‫ﻣﻲ‬‫ﺗﻮاﻧﺪ‬‫ﺑﺴﻴﺎر‬‫زﻣﺎﻧﺒﺮ‬‫ﺑﺎﺷﺪ‬.
••‫ﺗﺎﺑﻊ‬‫ﺗﺎﺑﻊ‬‫ارزﻳﺎﺑﻲ‬‫ارزﻳﺎﺑﻲ‬
f( n) = g( n) + h( n)f( n) g( n) h( n)
•g( n):‫ﻫﺰﻳﻨﻪ‬‫ﻣﺴﻴﺮ‬‫ﭘﻴﻤﻮده‬‫ﺷﺪه‬‫ﺗﺎ‬n.
•h( n):‫ﻫﺰﻳﻨﻪ‬‫ﺗﺨﻤﻴﻨﻲ‬‫ارزاﻧﺘﺮﻳﻦ‬‫ﻣﺴﻴﺮ‬‫راه‬‫ﺣﻞ‬‫از‬n‫ﺗﺎ‬‫ﻫﺪف‬‫ﻫﺪف‬. h( n):‫ﻫﺰﻳﻨﻪ‬‫ﺗﺨﻤﻴﻨﻲ‬‫ارزاﻧﺘﺮﻳﻦ‬‫ﻣﺴﻴﺮ‬‫راه‬‫ﺣﻞ‬‫از‬n‫ﺗﺎ‬‫ﻫﺪف‬‫ﻫﺪف‬.
•f( n):‫ﻫﺰﻳﻨﻪ‬‫ﺗﺨﻤﻴﻨﻲ‬‫ارزاﻧﺘﺮﻳﻦ‬‫راه‬‫ﺣﻞ‬‫ﻛﻪ‬‫از‬n‫ﻣﻲ‬‫ﮔﺬرد‬.
‫ﺟﺴﺘﺠﻮي‬ ‫ﻣﺜﺎل‬A*
Arad
366=0+366
393=140+253 449=75+374447=118+329
Arad Fagaras Oradea
Rimnicu
Vilcea
Craiova Pitesti
646=280+366 415=293+176
SibiuSibiu Bucharest
671=291+380 413=220+193
Craiova Pitesti
526=366+160 417=317+100
Sibiu
553=300+253
Sibiu Bucharest
591=338+253 450=450+0
Bucharest Craiova
418=418+0 615=455+160
Rimnicu
vilcea
607=414+193
418=418+0 615=455+160 607=414+193
‫ﺟﺴﺘﺠﻮي‬A* ‫ﺟﺴﺘﺠﻮي‬A*
A*‫از‬‫ﻳﻚ‬‫ﻫﻴﻮرﻳﺴﺘﻴﻚ‬‫ﻗﺎﺑﻞ‬‫ﻗﺎﺑﻞ‬‫ﻗﺒﻮل‬‫ﻗﺒﻮل‬(admissible)(admissible)‫اﺳﺘﻔﺎده‬‫ﻣﻲ‬،‫ﻛﻨﺪ‬
،‫ﻳﻌﻨﻲ‬‫ﻫﻤﻮاره‬h(n) ≤ h*(n)‫ﻛﻪ‬‫در‬‫آن‬h*(n)‫ﻫﺰﻳﻨﻪ‬‫واﻗﻌﻲ‬n‫ﺗﺎ‬‫ﻫﺪف‬ ،‫ﻳﻌﻨﻲ‬‫ﻫﻤﻮاره‬h(n) ≤ h (n)‫ﻛﻪ‬‫در‬‫آن‬h (n)‫ﻫﺰﻳﻨﻪ‬‫واﻗﻌﻲ‬n‫ﺗﺎ‬‫ﻫﺪف‬
‫ﻣﻲ‬‫ﺑﺎﺷﺪ‬.
‫ﻫﻢ‬‫ﭼﻨﻴﻦ‬‫دارﻳﻢ‬ ‫ﻢ‬‫ﻴﻦ‬ ‫ﭼ‬‫رﻳﻢ‬
h(n) ≥ 0‫و‬
h(G) = 0)G‫ﻚ‬‫ﻫﺪف‬‫ﺎﺷﺪ‬( h(G) = 0)G‫ﻳﻚ‬‫ﻫﺪف‬‫ﻣﻲ‬‫ﺑﺎﺷﺪ‬(.
‫ﺑﻪ‬‫ﻃﻮر‬‫ﻛﻠﻲ‬:
0 h( ) h*( )0 ≤ h(n) ≤ h*(n)
‫ﻣﺜﺎل‬:‫در‬‫ﻫﻴﻮرﻳﺴﺘﻴﻚ‬hSLD(n)‫ﻫﻴﭽﮕﺎه‬‫ﻫﺰﻳﻨﻪ‬‫ﺗﺨﻤﻴﻨﻲ‬‫ﺑﻴﺸﺘﺮ‬‫از‬‫ﻫﺰﻳﻨﻪ‬‫واﻗﻌﻲ‬
‫ا‬ ‫ﺨ‬)‫ﺎ‬ ‫ﻛ‬‫ﻠ‬ ‫ﻓﺎ‬‫ﻘﻄ‬‫ﻠ‬ ‫ﻓﺎ‬‫ﻘ‬‫آ‬‫ﻘﻄ‬ ‫ﻧﺨﻮاﻫﺪ‬‫ﺑﻮد‬.)‫ﻛﻮﺗﺎﻫﺘﺮﻳﻦ‬‫ﻓﺎﺻﻠﻪ‬‫ﺑﻴﻦ‬‫دو‬،‫ﻧﻘﻄﻪ‬‫ﻓﺎﺻﻠﻪ‬‫ﻣﺴﺘﻘﻴﻢ‬‫آن‬‫دو‬‫ﻧﻘﻄﻪ‬
‫ﻣﻲ‬‫ﺑﺎﺷﺪ‬(.

‫ﻗﺒﻮل‬ ‫ﻗﺎﺑﻞ‬ ‫ﻫﻴﻮرﻳﺴﺘﻴﻚ‬‫ﻗﺒﻮل‬ ‫ﻗﺎﺑﻞ‬ ‫ﻫﻴﻮرﻳﺴﺘﻴﻚ‬
•‫ﻳﻚ‬‫ﻚ‬ ‫ﺘ‬ ‫رﻳ‬ ‫ﻫ‬‫ﻣﺎﻧﻨﺪ‬h(n)‫ﻗﺎﺑﻞ‬‫ل‬ ‫ﻗ‬‫اﺳﺖ‬‫اﮔ‬‫اي‬ ‫ﺑ‬‫ﻫ‬‫ه‬ ‫ﮔ‬n‫داﺷﺘﻪ‬ •‫ﻳﻚ‬‫ﻫﻴﻮرﻳﺴﺘﻴﻚ‬‫ﻣﺎﻧﻨﺪ‬h(n)‫ﻗﺎﺑﻞ‬‫ﻗﺒﻮل‬‫اﺳﺖ‬‫اﮔﺮ‬‫ﺑﺮاي‬‫ﻫﺮ‬‫ﮔﺮه‬n‫داﺷﺘﻪ‬
‫ﺑﺎﺷﻴﻢ‬:h(n) ≤ h*(n)‫ﻛﻪ‬h*(n)‫ﻫﺰﻳﻨﻪ‬‫واﻗﻌﻲ‬‫ﺑﺮاي‬‫رﺳﻴﺪن‬‫ﺑﻪ‬‫ﻫﺪف‬‫از‬
‫ﮔﺮه‬n‫ﻣﻲ‬‫ﺑﺎﺷﺪ‬. ‫ﮔﺮه‬n‫ﻣﻲ‬‫ﺑﺎﺷﺪ‬.
•‫ﻳﻚ‬‫ﻚ‬ ‫ﺘ‬ ‫ﻮرﻳ‬ ‫ﻫ‬‫ﻗﺎﺑﻞ‬‫ﻮل‬ ‫ﻗ‬‫ﮔﺰ‬ ‫ﻫ‬‫ﻫﺰﻳﻨﻪ‬‫ﺪن‬ ‫رﺳ‬‫ﺑﻪ‬‫ﻫﺪف‬‫را‬‫ﺶ‬ ‫ﺑ‬‫از‬‫ﺣﺪ‬ ‫ﻳﻚ‬‫ﻫﻴﻮرﻳﺴﺘﻴﻚ‬‫ﻗﺎﺑﻞ‬‫ﻗﺒﻮل‬‫ﻫﺮﮔﺰ‬‫ﻫﺰﻳﻨﻪ‬‫رﺳﻴﺪن‬‫ﺑﻪ‬‫ﻫﺪف‬‫را‬‫ﺑﻴﺶ‬‫از‬‫ﺣﺪ‬
‫ﺗﺨﻤﻴﻦ‬‫ﻧﻤﻲ‬‫زﻧﺪ‬،‫ﻳﻌﻨﻲ‬‫ﺧﻮش‬‫ﺑﻴﻨﺎﻧﻪ‬‫اﺳﺖ‬.
•‫ﻣﺜﺎل‬:‫ﻫﻴﻮرﻳﺴﺘﻴﻚ‬hSLD(n))‫ﻫﻴﭽﮕﺎه‬‫ﻓﺎﺻﻠﻪ‬‫واﻗﻌﻲ‬‫را‬‫ﺑﻴﺶ‬‫از‬‫ﺣﺪ‬‫ﺗﺨﻤﻴﻦ‬
‫ﻧ‬‫زﻧﺪ‬( ‫ﻧﻤﻲ‬‫زﻧﺪ‬(.
‫ﮔ‬h( )‫ﻞ‬‫ل‬A*TREE SEARCH •‫ﻗﻀﻴﻪ‬:‫اﮔﺮ‬h(n)‫ﻗﺎﺑﻞ‬‫ﻗﺒﻮل‬،‫ﺑﺎﺷﺪ‬A*‫ﺑﺎ‬‫اﺳﺘﻔﺎده‬‫از‬TREE-SEARCH
‫ﺑﻬﻴﻨﻪ‬‫اﺳﺖ‬.
‫ﺑﻬﻴﻨﮕ‬A*)‫اﺛﺒﺎت‬( ‫ﺑﻬﻴﻨﮕﻲ‬A*)‫اﺛﺒﺎت‬(
•‫ﻓﺮض‬‫ﻛﻨﻴﺪ‬‫ﻳﻚ‬‫ﺟﻮاب‬‫زﻳﺮ‬‫ﺑﻬﻴﻨﻪ‬‫ﻣﺎﻧﻨﺪ‬G2‫ﺗﻮﻟﻴﺪ‬‫ﺷﺪه‬‫و‬‫در‬‫ﺻﻒ‬‫ﻗﺮار‬‫دارد‬.
‫ﻫﻢ‬‫ﭼﻨﻴﻦ‬‫ﻓﺮض‬‫ﻛﻨﻴﺪ‬n‫ﻳﻚ‬‫ﮔﺮه‬‫ﮔﺴﺘﺮش‬‫ﻧﻴﺎﻓﺘﻪ‬‫روي‬‫ﻛﻮﺗﺎﻫﺘﺮﻳﻦ‬‫ﻣﺴﻴﺮ‬‫ﺑﻪ‬‫ﻫﺪف‬‫ﺑﻬﻴﻨﻪ‬G‫ﺑﺎﺷﺪ‬)‫؟؟‬( ‫ﻢ‬
f(G2) = g(G2) ،‫ﭼﻮن‬h(G2) = 0
> g(G)
≥ f(n)
f( ) f( )‫ﮕ‬*‫ﮔ‬
،‫ﭼﻮن‬G2‫اﺳﺖ‬ ‫ﺑﻬﻴﻨﻪ‬ ‫زﻳﺮ‬
،‫ﭼﻮن‬h‫اﺳﺖ‬ ‫ﻗﺒﻮل‬ ‫ﻗﺎﺑﻞ‬
‫ﭼﻮن‬f(G2) ≥ f(n)،‫اﻟﮕﻮرﻳﺘﻢ‬A*‫ﻫﻴﭻ‬‫وﻗﺖ‬G2‫را‬‫ﺑﺮاي‬‫ﮔﺴﺘﺮش‬‫ﻳﺎﻓﺘﻦ‬‫اﻧﺘﺨﺎب‬‫ﻧﻤﻲ‬‫ﻛﻨﺪ‬.
‫ﻟﻢ‬ ‫اﺛﺒﺎت‬:‫ﺳﺎزﮔﺎري‬(Consistency)( y)
•‫ﻳﻚ‬‫ﻫﻴﻮرﻳﺴﺘﻴﻚ‬‫ﺳﺎزﮔﺎر‬‫ﺳﺎزﮔﺎر‬‫اﺳﺖ‬‫اﮔﺮ‬:h(n) ≤ c(n, a, n’) + h(n’) ‫ﻳ‬‫ﻴ‬ ‫ﻴﻮرﻳ‬‫ر‬ ‫ز‬‫ر‬ ‫ز‬‫ﺮ‬( ) ( , , ) ( )
•‫اﮔﺮ‬h‫ﺳﺎزﮔﺎر‬،‫ﺑﺎﺷﺪ‬‫دارﻳﻢ‬:
f(n’) = g(n’) + h(n’)
= g(n) + c(n, a, n’) + h(n’)
≥ g(n) + h(n)
≥ f(n)≥ f(n)
•،‫ﻳﻌﻨﻲ‬f(n)‫در‬‫ﻃﻮل‬‫ﻫﺮ‬‫ﻣﺴﻴﺮي‬‫ﻏﻴﺮ‬‫ﻛﺎﻫﺸﻲ‬‫ﻣﻲ‬‫ﺑﺎﺷﺪ‬.)،‫ﻳﻜﻨﻮاﻳﻲ‬monotonicity(
•‫ﻗﻀﻴﻪ‬:‫اﮔﺮ‬h(n)‫ﺳﺎزﮔﺎر‬،‫ﺑﺎﺷﺪ‬A*‫ﺑﺎ‬‫اﺳﺘﻔﺎده‬‫از‬GRAPH-SEARCH‫ﺑﻬﻴﻨﻪ‬‫اﺳﺖ‬.
‫ﺑﻬﻴﻨﮕﻲ‬A*
••‫ﻟﻢ‬‫ﻟﻢ‬::A*‫ﮔﺮه‬‫ﻫﺎ‬‫را‬‫ﺑﺮاﺳﺎس‬‫ﺗﺮﺗﻴﺐ‬‫ﺻﻌﻮدي‬‫ﻣﻘﺎدﻳﺮ‬f‫ﮔﺴﺘﺮش‬‫ﻣﻲ‬‫دﻫﺪ‬.
•A*‫ﺑﻪ‬‫ﺗﺪرﻳﺞ‬f-contour‫ﻫﺎ‬‫را‬‫اﺿﺎﻓﻪ‬‫ﻣﻲ‬‫ﻛﻨﺪ‬.
•‫ﻛﺎﻧﺘﻮر‬i‫ﺷﺎﻣﻞ‬‫ﺗﻤﺎم‬‫ﮔﺮه‬‫ﻫﺎ‬‫ﺑﺎ‬f = fi‫ﻣﻲ‬،‫ﺑﺎﺷﺪ‬‫ﻛﻪ‬fi < fi+1

‫ﺧﺼﻮﺻﻴﺎت‬A* ‫ﻴ‬ ‫ﻮ‬
•‫ﻛﺎﻣﻞ؟‬،‫ﺑﻠﻪ‬‫ﻣﮕﺮ‬‫اﻳﻨﻜﻪ‬‫ﺗﻌﺪادي‬‫ﻧﺎﻣﺤﺪود‬‫ﮔﺮه‬‫ﺑﺎ‬f ≤ f(G)‫وﺟﻮد‬‫داﺷﺘﻪ‬
A*‫ﮔ‬‫ﻛ‬‫ﻛ‬ ‫ﺑﺎﺷﺪ‬.A*‫در‬‫ﮔﺮاف‬‫ﻫﺎي‬‫ﻣﺘﻨﺎﻫﻲ‬‫ﻣﺤﻠﻲ‬)‫ﺑﺎ‬‫ﻓﺎﻛﺘﻮر‬‫اﻧﺸﻌﺎب‬‫ﻣﺤﺪود‬(‫ﻛﺎﻣﻞ‬
‫اﺳﺖ‬‫ﺑﻪ‬‫ﺷﺮط‬‫آﻧﻜﻪ‬‫ﻫﺰﻳﻨﻪ‬‫ﺗﻤﺎم‬‫ﻋﻤﻠﮕﺮﻫﺎ‬‫ﻣﺜﺒﺖ‬‫ﺑﺎﺷﺪ‬. ‫م‬
-‫ﮔﺮه‬‫اي‬‫ﺑﺎ‬‫ﻓﺎﻛﺘﻮر‬‫اﻧﺸﻌﺎب‬‫ﻧﺎﻣﺤﺪود‬‫وﺟﻮد‬‫داﺷﺘﻪ‬‫ﺑﺎﺷﺪ‬.
‫ي‬‫ﺎ‬‫ﻨﻪ‬ ‫ﻫﺰ‬‫د‬ ‫ﺪ‬‫ﺎ‬ ‫ا‬‫ﺎ‬‫ﺪاد‬ ‫ﺗ‬‫ﮔ‬‫ﻫﺎي‬‫د‬ ‫ﺪ‬ ‫ﻧﺎ‬‫د‬‫داﺷﺘﻪ‬‫ﺎﺷﺪ‬ -‫ﻣﺴﻴﺮي‬‫ﺑﺎ‬‫ﻫﺰﻳﻨﻪ‬‫ﻣﺤﺪود‬‫اﻣﺎ‬‫ﺑﺎ‬‫ﺗﻌﺪاد‬‫ﮔﺮه‬‫ﻫﺎي‬‫ﻧﺎﻣﺤﺪود‬‫وﺟﻮد‬‫داﺷﺘﻪ‬‫ﺑﺎﺷﺪ‬.
•‫ﭘﻴﭽﻴﺪﮔﻲ‬‫زﻣﺎﻧﻲ؟‬‫ﻧﻤﺎﻳﻲ‬‫ﺑﺮ‬‫ﺣﺴﺐ‬]‫ﺧﻄﺎي‬‫ﻧﺴﺒﻲ‬h*‫ﻃﻮل‬‫راه‬‫ﺣﻞ‬[
‫ﮕ‬‫ﻨﻜﻪ‬ ‫ا‬‫ﺧﻄﺎ‬‫ﺗﺎ‬‫ﻛﺸﻒ‬‫ﻛﻨﻨﺪ‬‫ﺷﺪ‬‫ﺘ‬‫از‬‫ﺘ‬ ‫ﻟﮕﺎ‬‫ﻨﻪ‬ ‫ﺰ‬ ‫ﻣﮕﺮ‬‫اﻳﻨﻜﻪ‬‫ﺧﻄﺎ‬‫در‬‫ﺗﺎﺑﻊ‬‫ﻛﺸﻒ‬‫ﻛﻨﻨﺪه‬‫رﺷﺪي‬‫ﺳﺮﻳﻌﺘﺮ‬‫از‬‫ﻟﮕﺎرﻳﺘﻢ‬‫ﻫﺰﻳﻨﻪ‬‫ﻣﺴﻴﺮ‬
‫واﻗﻌﻲ‬‫ﻧﺪاﺷﺘﻪ‬،‫ﺑﺎﺷﺪ‬‫ﺑﻪ‬‫زﺑﺎن‬‫رﻳﺎﺿﻲ‬:
| h(n) – h*(n)| ≤ O (log h*(n))
‫ﺧﺼﻮﺻﻴﺎت‬A* ‫ﻴ‬ ‫ﻮ‬
•‫ﭘﻴﭽﻴﺪﮔﻲ‬‫ﻓﻀﺎ؟؟‬‫ﺗﻤﺎم‬‫ﮔﺮه‬‫ﻫﺎ‬‫را‬‫در‬‫ﺣﺎﻓﻈﻪ‬‫ﻧﮕﻪ‬‫ﻣﻲ‬‫دارد‬.
f‫ﮔ‬‫ﮕ‬f •‫ﺑﻬﻴﻨﻪ؟؟‬‫ﺑﻠﻪ‬–‫ﻧﻤﻲ‬‫ﺗﻮاﻧﺪ‬fi+1‫را‬‫ﮔﺴﺘﺮش‬‫دﻫﺪ‬‫ﻣﮕﺮ‬fi‫ﺗﻤﺎم‬‫ﺷﺪه‬
‫ﺑﺎﺷﺪ‬. ‫ﺑ‬
–A*‫ﺗﻤﺎم‬‫ﮔﺮه‬‫ﻫﺎ‬‫ﺑﺎ‬f(n) < f*‫را‬‫ﮔﺴﺘﺮش‬‫ﻣﻲ‬‫دﻫﺪ‬.
*‫ﮔ‬f( ) f*‫ﮔ‬ –A*‫ﺑﺮﺧﻲ‬‫از‬‫ﮔﺮه‬‫ﻫﺎ‬‫ﺑﺎ‬f(n) = f*‫را‬‫ﮔﺴﺘﺮش‬‫ﻣﻲ‬‫دﻫﺪ‬.
–A*‫ﮔﺮه‬‫اي‬‫ﺑﺎ‬f(n) > f*‫را‬‫ﻫﺮﮔﺰ‬‫ﮔﺴﺘﺮش‬‫ﻧﻤﻲ‬‫دﻫﺪ‬. ‫ﺮ‬‫ي‬‫ﺑ‬( )‫ر‬‫ﺰ‬ ‫ﺮ‬‫ﺮش‬‫ﻤﻲ‬.
•A*‫داراي‬‫ﻛﺎرآﻳﻲ‬‫ﻛﺎرآﻳﻲ‬‫ﺑﻬﻴﻨﻪ‬‫ﺑﻬﻴﻨﻪ‬(optimally efficient)‫ﻣﻲ‬‫ﺑﺎﺷﺪ‬!
‫ﻣﺤﺪود‬ ‫ﺣﺎﻓﻈﻪ‬ ‫ﺑﺎ‬ ‫ﻫﻴﻮرﻳﺴﺘﻴﻚ‬ ‫ﺟﺴﺘﺠﻮي‬‫ﻣﺤﺪود‬ ‫ﺣﺎﻓﻈﻪ‬ ‫ﺑﺎ‬ ‫ﻫﻴﻮرﻳﺴﺘﻴﻚ‬ ‫ﺟﺴﺘﺠﻮي‬
•‫ﭼﻨﺪ‬‫راه‬‫ﺣﻞ‬‫ﺑﺮاي‬‫ﻣﺴﺄﻟﻪ‬‫ﺣﺎﻓﻈﻪ‬‫در‬A*)‫ﺑﺎ‬‫ﺣﻔﻆ‬‫ﺧﺼﻮﺻﻴﺎت‬
‫ﻛﺎﻣﻞ‬‫ﺑﻮدن‬‫و‬‫ﺑﻬﻴﻨﮕ‬(: ‫ﻛﺎﻣﻞ‬‫ﺑﻮدن‬‫و‬‫ﺑﻬﻴﻨﮕﻲ‬(:
–‫ﺟﺴﺘﺠﻮي‬‫ﻋﻤﻴﻖ‬‫ﻛﻨﻨﺪه‬‫ﺗﻜﺮاي‬A*(IDA*)
•‫ﻣﺎﻧﻨﺪ‬IDS‫وﻟﻲ‬‫ﺑﻪ‬‫ﺟﺎي‬‫ﻣﺤﺪوده‬‫ﻋﻤﻘﻲ‬‫از‬‫ﻣﺤﺪوده‬f-cost(g + h)‫اﺳﺘﻔﺎده‬
‫ﻣﻲ‬‫ﺷﻮد‬
‫ﮔ‬ –‫ﺟﺴﺘﺠﻮي‬‫اول‬-‫ﺑﻬﺘﺮﻳﻦ‬‫ﺑﺎزﮔﺸﺘﻲ‬(RBFS)
•‫ﻳﻚ‬‫اﻟﮕﻮرﻳﺘﻢ‬‫ﺑﺎزﮔﺸﺘﻲ‬‫ﺑﺎ‬‫ﻓﻀﺎي‬‫ﺧﻄﻲ‬‫ﻛﻪ‬‫ﺳﻌﻲ‬‫ﻣﻲ‬‫ﻛﻨﺪ‬‫از‬‫ﺟﺴﺘﺠﻮي‬ ‫ﻢ‬
‫اول‬-‫ﺑﻬﺘﺮﻳﻦ‬‫اﺳﺘﺎﻧﺪارد‬‫ﺗﻘﻠﻴﺪ‬‫ﻛﻨﺪ‬
–‫ﺟﺴﺘﺠﻮي‬)‫ﺳﺎده‬‫ﺷﺪه‬(A*‫ﺑﺎ‬‫ﺣﺎﻓﻈﻪ‬‫ﻣﺤﺪود‬((S)MBA*) ‫ﻮ‬‫ﺑ‬(( ) )
•‫ﺣﺬف‬‫ﺑﺪﺗﺮﻳﻦ‬‫ﮔﺮه‬‫وﻗﺘﻲ‬‫ﻛﻪ‬‫ﺣﺎﻓﻈﻪ‬‫ﭘﺮ‬‫ﻣﻲ‬‫ﺑﺎﺷﺪ‬
‫اول‬ ‫ﺟﺴﺘﻮي‬-‫ﺑﺎزﮔﺸﺘﻲ‬ ‫ﺑﻬﺘﺮﻳﻦ‬(RBFS) ‫ول‬ ‫ﻮي‬ ‫ﺟ‬‫ﻲ‬ ‫ز‬ ‫ﺑ‬ ‫ﺮﻳﻦ‬ ‫ﺑﻬ‬( )
function RECURSIVE-BEST-FIRST-SEARCH(problem) return a solution or failure
( bl ( bl ) )return RFBS(problem,MAKE-NODE(INITIAL-STATE[problem]),∞)
function RFBS( problem node f limit) return a solution or failure and a new f-cost limitfunction RFBS( problem, node, f_limit) return a solution or failure and a new f-cost limit
if GOAL-TEST[problem](STATE[node]) then return node
successors ← EXPAND(node, problem)successors ← EXPAND(node, problem)
if successors is empty then return failure, ∞
for each s in successors do
f [s] ← max(g(s) + h(s), f [node])
repeat
b h l f l d ibest ← the lowest f-value node in successors
if f [best] > f_limit then return failure, f [best]
alternative ← the second lowest f-value among successorsalternative ← the second lowest f value among successors
result, f [best] ← RBFS(problem, best, min(f_limit, alternative))
if result ≠ failure then return result

‫ﺟﺴﺘﺠﻮي‬RBFS ‫ﺟﺴﺘﺠﻮي‬RBFS
•‫ﻣﻘﺪار‬f-value‫ﻣﺮﺑﻮط‬‫ﺑﻪ‬‫ﺑﻬﺘﺮﻳﻦ‬‫ﻣﺴﻴﺮ‬‫ﺟﺎﻳﮕﺰﻳﻦ‬‫ﻣﻮﺟﻮد‬‫را‬
‫ﺪا‬ ‫ﻧﮕ‬‫ﻛﻨﺪ‬ ‫ﻧﮕﻬﺪاري‬‫ﻣﻲ‬‫ﻛﻨﺪ‬.
–‫اﮔﺮ‬f-value‫ﻓﻌﻠﻲ‬‫از‬f-value‫ﻣﺴﻴﺮ‬‫ﺟﺎﻳﮕﺰﻳﻦ‬‫ﺗﺠﺎوز‬،‫ﻛﻨﺪ‬‫آﻧﮕﺎه‬‫ﺑﻪ‬ ‫ﺮ‬‫ﻲ‬‫ز‬‫ﻴﺮ‬‫ﺰﻳﻦ‬ ‫ﻳ‬ ‫ﺟ‬‫وز‬ ‫ﺠ‬‫ﺑ‬
‫اﻳﻦ‬‫ﻣﺴﻴﺮ‬‫ﺟﺎﻳﮕﺰﻳﻦ‬‫ﺑﺎزﮔﺸﺖ‬‫ﻣﻲ‬‫ﻛﻨﺪ‬.
‫ﺎزﮔﺸﺖ‬‫ﻪ‬‫ﻘ‬‫ﻘﺪا‬f value‫ط‬‫ﻪ‬‫ﮔ‬ –‫در‬‫ﺑﺎزﮔﺸﺖ‬‫ﺑﻪ‬‫ﻋﻘﺐ‬‫ﻣﻘﺪار‬f-value‫ﻣﺮﺑﻮط‬‫ﺑﻪ‬‫ﻫﺮ‬‫ﮔﺮه‬‫ﻣﻮﺟﻮد‬‫در‬
‫ﻣﺴﻴﺮ‬‫را‬‫ﺑﺎ‬‫ﺑﻬﺘﺮﻳﻦ‬‫ﻣﻘﺪار‬f-value‫ﻓﺮزﻧﺪاﻧﺶ‬‫ﺟﺎﻳﮕﺰﻳﻦ‬‫ﻣﻲ‬‫ﻛﻨﺪ‬.
–‫ﺑﻨﺎﺑﺮاﻳﻦ‬‫ﮔﺴﺘﺮش‬‫ﻣﺠﺪد‬‫ﻧﺘﻴﺠﻪ‬‫ﻓﻌﻠﻲ‬‫ﻫﻨﻮز‬‫ﻣﻤﻜﻦ‬‫ﻣﻲ‬‫ﺑﺎﺷﺪ‬.
‫ﺟﺴﺘﺠﻮي‬ ‫ﻣﺜﺎل‬RBFS
Arad
‫ﺠﻮي‬ ‫ﺟ‬ ‫ل‬
∞
366
447
393 449447
Rimnicu
VilceaArad Fagaras Oradea
413
415
417
i
646 415 526 413 417
Craiova Pitesti
526 417
Sibiu
553
‫ﺟﺴﺘﺠﻮي‬ ‫ﻣﺜﺎل‬RBFS ‫ﺠﻮي‬ ‫ﺟ‬ ‫ل‬
Arad
∞
366
447
393 449447
FagarasArad Oradea
Rimnicu
Vilcea
413
417
417646 415
Sibi B h
526 413 417450
Sibiu Bucharest
591 450
‫ﺟﺴﺘﺠﻮي‬ ‫ﻣﺜﺎل‬RBFS ‫ﺠﻮي‬ ‫ﺟ‬ ‫ل‬
Arad
∞
366
447
393 449447
Arad Fagaras Oradea
Rimnicu
Vilcea
417
447
i
646 526 417
447
450
Craiova Pitesti
526 417
Sibiu
553
Bucharest Craiova
418 615
Rimnicu
vilcea
607
418 615 607

‫ارزﻳﺎﺑ‬RBFS ‫ارزﻳﺎﺑﻲ‬RBFS
•RBFS‫ﻛﻤﻲ‬‫ﻛﺎرآﺗﺮ‬‫از‬IDA*‫ﻣﻲ‬‫ﺑﺎﺷﺪ‬
–‫ﻫﻨﻮز‬‫ﮔﺴﺘﺮش‬‫اﺿﺎﻓﻲ‬‫ﮔﺮه‬‫ﻫﺎ‬‫وﺟﻮد‬‫دارد‬)‫ﺗﻐﻴﻴﺮ‬‫ﻋﻘﻴﺪه‬( ‫ﻮ‬‫ﺮش‬‫ﻲ‬‫ﺮ‬‫ﻮ‬‫ﻴﻴﺮ‬‫ﻴ‬
•RBFS‫ﻫﻢ‬‫ﻣﺎﻧﻨﺪ‬A*،‫اﮔﺮ‬h(n)‫ﻗﺎﺑﻞ‬‫ﻗﺒﻮل‬‫ﺑﺎﺷﺪ‬‫ﺑﻬﻴﻨﻪ‬‫اﺳﺖ‬
•‫ﭘﻴﭽﻴﺪﮔ‬‫ﺣﺎﻓﻈﻪ‬O(bd) ‫ﭘﻴﭽﻴﺪﮔﻲ‬‫ﺣﺎﻓﻈﻪ‬O(bd)
–IDA*‫ﻓﻘﻂ‬‫ﻳﻚ‬‫ﻋﺪد‬‫را‬‫ﻧﮕﻬﺪاري‬‫ﻣﻲ‬‫ﻛﻨﺪ‬)‫ﺣﺪ‬‫ﻓﻌﻠﻲ‬f-cost(
•‫ﺗﻌﻴﻴﻦ‬‫ﭘﻴﭽﺪﮔ‬‫زﻣﺎﻧ‬‫ﻣﺸﻜﻞ‬‫اﺳﺖ‬ ‫ﺗﻌﻴﻴﻦ‬‫ﭘﻴﭽﺪﮔﻲ‬‫زﻣﺎﻧﻲ‬‫ﻣﺸﻜﻞ‬‫اﺳﺖ‬
–‫ﺑﻪ‬‫دﻗﺖ‬‫ﺗﺎﺑﻊ‬‫ﻫﻴﻮرﻳﺴﺘﻴﻚ‬‫و‬‫ﻣﻴﺰان‬‫ﺗﻐﻴﻴﺮ‬‫ﺑﻬﺘﺮﻳﻦ‬‫ﻣﺴﻴﺮ‬‫در‬‫اﺛﺮ‬‫ﺑﺴﻂ‬‫ﮔﺮه‬‫ﻫﺎ‬‫ﺑﺴﺘﮕﻲ‬
‫دارد‬. ‫ر‬
–‫ﻣﺎﻧﻨﺪ‬IDA*‫در‬‫ﻣﻌﺮض‬‫اﻓﺰاﻳﺶ‬‫ﻧﻤﺎﻳﻲ‬‫ﭘﻴﭽﻴﺪﮔﻲ‬‫زﻣﺎﻧﻲ‬‫ﻗﺮار‬‫دارد‬
•RBFS‫و‬IDA*‫ﻫﺮ‬‫دو‬‫از‬‫ﻣﻴﺰان‬‫ﺑﺴﻴﺎر‬‫ﻛﻢ‬‫ﺣﺎﻓﻈﻪ‬‫رﻧﺞ‬‫ﻣﻲ‬‫ﺑﺮﻧﺪ‬. S‫و‬‫ﺮ‬‫و‬‫ز‬‫ن‬ ‫ﻴﺰ‬‫ر‬ ‫ﻴ‬ ‫ﺑ‬‫ﻢ‬‫ﺞ‬‫ر‬‫ﻲ‬‫ﺑﺮ‬.
–‫ﺑﺎ‬‫اﻳﻨﻜﻪ‬‫ﺣﺎﻓﻈﻪ‬‫زﻳﺎدي‬‫وﺟﻮد‬،‫دارد‬‫ﻧﻤﻲ‬‫ﺗﻮاﻧﻨﺪ‬‫از‬‫ان‬‫اﺳﺘﻔﺎده‬‫ﻛﻨﻨﺪ‬.
(Simplified) Memory Bounded A*( p ) y
•‫اﻳﺪه‬:‫اﺳﺘﻔﺎده‬‫از‬‫ﺗﻤﺎﻣﻲ‬‫ﺣﺎﻓﻈﻪ‬‫ﻣﻮﺟﻮد‬
، ‫ﻨ‬‫ش‬ ‫ﺘ‬ ‫ﮔ‬‫ﻦ‬ ‫ﻬﺘ‬‫ﮔ‬‫ﻫﺎي‬‫ﮔ‬‫ﺗﺎ‬‫ﺎﻧ‬ ‫ز‬‫ﻛﻪ‬‫ﺎﻓﻈﻪ‬‫د‬‫د‬ ‫ﺷ‬ –،‫ﻳﻌﻨﻲ‬‫ﮔﺴﺘﺮش‬‫ﺑﻬﺘﺮﻳﻦ‬‫ﮔﺮه‬‫ﻫﺎي‬‫ﺑﺮﮔﻲ‬‫ﺗﺎ‬‫زﻣﺎﻧﻲ‬‫ﻛﻪ‬‫ﺣﺎﻓﻈﻪ‬‫ﻣﻮﺟﻮد‬‫ﭘﺮ‬‫ﺷﻮد‬
–‫در‬‫ﺻﻮرت‬‫ﭘﺮ‬‫ﺷﺪن‬،‫ﺣﺎﻓﻈﻪ‬SMA*‫ﺑﺪﺗﺮﻳﻦ‬‫ﮔﺮه‬‫ﺑﺮگ‬)‫ﺑﺎ‬‫ﺑﻴﺸﺘﺮﻳﻦ‬‫ﻣﻘﺪار‬f-value(‫را‬
‫از‬‫ﺣﺎﻓﻈﻪ‬‫ﺣﺬف‬‫ﻣﻲ‬‫ﻛﻨﺪ‬ ‫ز‬‫ﻲ‬
–‫ﻣﺎﻧﻨﺪ‬RBFS‫اﻃﻼﻋﺎت‬‫ﮔﺮه‬‫ﻫﺎي‬‫ﻓﺮاﻣﻮش‬‫ﺷﺪه‬‫را‬‫در‬‫ﭘﺪرﺷﺎن‬‫ذﺧﻴﺮه‬‫ﻣﻲ‬‫ﻛﻨﺪ‬
•‫اﮔﺮ‬‫ﺗﻤﺎم‬‫ﺑﺮگ‬‫ﻫﺎ‬‫داراي‬f-value‫ﺑﺮاﺑﺮ‬‫ﺑﺎﺷﻨﺪ‬‫ﭼﻪ‬‫ﻣﻲ‬‫ﺷﻮد؟‬
–‫ﻣﻤﻜﻦ‬‫اﺳﺖ‬‫ﻳﻚ‬‫ﮔﺮه‬‫را‬‫ﻫﻢ‬‫ﺑﺮاي‬‫ﮔﺴﺘﺮش‬‫و‬‫ﻫﻢ‬‫ﺑﺮاي‬‫ﺣﺬف‬‫اﻧﺘﺨﺎب‬‫ﻛﻨﺪ‬ ‫ﻦ‬‫ﻳ‬‫ﺮ‬‫ر‬‫ﻢ‬‫ي‬ ‫ﺑﺮ‬‫ﺮش‬‫و‬‫ﻢ‬‫ي‬ ‫ﺑﺮ‬‫ف‬‫ب‬
–‫راه‬‫ﺣﻞ‬SMA*:
•‫ﮔﺴﺘﺮش‬:‫ﺑﻬﺘﺮﻳﻦ‬‫ﮔﺮه‬‫ﺑﺮگ‬‫ﻛﻪ‬‫از‬‫ﻫﻤﻪ‬‫ﺟﺪﻳﺪ‬‫ﺗﺮ‬‫اﺳﺖ‬
•‫ﺣﺬف‬:‫ﺑﺪﺗﺮﻳﻦ‬‫ﮔﺮه‬‫ﻛﻪ‬‫از‬‫ﻫﻤﻪ‬‫ﻗﺪﻳﻤﻲ‬‫ﺗﺮ‬‫اﺳﺖ‬
SMA*‫ﻞ‬ ‫ﻛﺎ‬‫ﺖ‬ ‫ا‬‫اﮔ‬‫ا‬‫ﻞ‬‫ﻞ‬‫ﻗﺎ‬‫ﺎ‬‫اﺷ‬‫ﺎﺷﺪ‬‫ﺖ‬ ‫ا‬‫اﮔ‬‫ا‬ –SMA*‫ﻛﺎﻣﻞ‬‫اﺳﺖ‬‫اﮔﺮ‬‫راه‬‫ﺣﻞ‬‫ﻗﺎﺑﻞ‬‫دﺳﺘﻴﺎﺑﻲ‬‫وﺟﻮد‬‫داﺷﺘﻪ‬‫ﺑﺎﺷﺪ‬‫و‬‫ﺑﻬﻴﻨﻪ‬‫اﺳﺖ‬‫اﮔﺮ‬‫راه‬
‫ﺣﻞ‬‫ﺑﻬﻴﻨﻪ‬‫ﻗﺎﺑﻞ‬‫دﺳﺘﻴﺎﺑﻲ‬‫ﺑﺎﺷﺪ‬
‫ﻗﺒﻮل‬ ‫ﻗﺎﺑﻞ‬ ‫ﻫﺎي‬ ‫ﻫﻴﻮرﻳﺴﺘﻴﻚ‬‫ﻗﺒﻮل‬ ‫ﻗﺎﺑﻞ‬ ‫ﻫﺎي‬ ‫ﻫﻴﻮرﻳﺴﺘﻴﻚ‬
‫ﻣﺜﺎل‬‫ﺑﺮاي‬‫ﻣﻌﻤﺎي‬‫ﻫﺸﺖ‬:
h (n) = number of misplaced tilesh1(n) number of misplaced tiles
h2(n) = total ManhattanManhattan distance
hh11(S)(S) = 6
hh22(S)(S) = 4 + 0 + 3 + 3 + 1 + 0 + 2 + 1 = 14
2 8 3
1 6 4
7 5
0+4 •‫ﻣﺜﺎل‬:‫ﺟﺴﺘﺠﻮي‬A*‫ﺑﺎ‬h1
2 8 32 8 3 2 8 3
7 _ 5
2 8 3
1 _ 4
7 6 5
2 8 3
1 6 4
_ 7 5
2 8 3
1 6 4
7 5 _
1+5 1+3 1+5
2 _ 3
1 8 4
7 6 5
2 8 3
1 4 _
7 6 5
2 8 3
_ 1 4
7 6 5
2+3 2+3 2+4
7 6 5
2 3_ 2 3
1 8 4
7 6 5
_ 8 3
2 1 4
7 6 5
2 3 _
1 8 4
7 6 5
2 8 3
7 1 4
- 6 5
3+3 3+4 3+2 3+4
1 2 3
_ 8 4
7 6 5
4+1
1 2 3
8 _ 4
7 6 5
1 2 3
7 8 45+0 5+2
7 6 5
_ 6 5

2 8 3
1 6 4
7 5
0+5
•‫ﻣﺜﺎل‬:‫ﺟﺴﺘﺠﻮي‬A*‫ﺑﺎ‬h2
2 8 3
7 _ 5
2 8 3 2 8 32 8 3
1 _ 4
7 6 5
2 8 3
1 6 4
_ 7 5
2 8 3
1 6 4
7 5 _
1+6 1+4 1+6
2 _ 3
1 8 4
7 6 5
2 8 3
1 4 _
7 6 5
2 8 3
_ 1 4
7 6 5
2+5 2+3 2+5
7 6 5 7 6 57 6 5
2 3_ 2 3
1 8 4
7 6 5
2 3 _
1 8 4
7 6 5
3+2 3+4
1 2 3
_ 8 4
7 6 5
4+1
1 2 3
8 _ 4
1 2 3
7 8 45+0 5+2
7 6 5
7 8 4
_ 6 5
5+2
‫ﺗﺴﻠﻂ‬(Dominance) ‫ﺗﺴﻠﻂ‬(Dominance)
•‫اﮔﺮ‬‫ﺑﺎزاء‬‫ﻫﺮ‬n‫داﺷﺘﻪ‬،‫ﺑﺎﺷﻴﻢ‬h2(n) ≥ h1(n)
)‫و‬‫ﻫﺮ‬‫دو‬‫ﻫﻴﻮرﻳﺴﺘﻴﻚ‬‫ﻗﺎﺑﻞ‬‫ﻗﺒﻮل‬‫ﺑﺎﺷﻨﺪ‬( ‫ﻞ‬
•‫آﻧﮕﺎه‬h2‫ﺑﺮ‬h1‫ﺗﺴﻠﻂ‬‫ﺗﺴﻠﻂ‬‫دارد‬‫و‬‫ﺑﺮاي‬‫ﺟﺴﺘﺠﻮ‬‫ﺑﻬﺘﺮ‬‫ﻣﻲ‬‫ﺑﺎﺷﺪ‬.
•‫ﻣﺜﺎل‬‫ﻫﺰﻳﻨﻪ‬‫ﺟﺴﺘﺠﻮ‬:‫ﺗﻌﺪاد‬‫ﮔﺮه‬‫ﻫﺎي‬‫ﺗﻮﻟﻴﺪ‬‫ﺷﺪه‬‫در‬‫ﻋﻤﻖ‬‫ﻫﺎي‬‫ﻣﺨﺘﻠﻒ‬:
d = 14 IDS = 3,473,941
A*(h1) = 539
A*(h2) = 113
d = 24 IDS = 54,000,000,000
A*(h ) 39 135A*(h1) = 39,135
A*(h2) = 1641
‫ﻣﻮﺛﺮ‬ ‫اﻧﺸﻌﺎب‬ ‫ﻓﺎﻛﺘﻮر‬(b*) ‫ﻣﻮﺛﺮ‬ ‫اﻧﺸﻌﺎب‬ ‫ﻓﺎﻛﺘﻮر‬(b*)
•‫ﻓﺎﻛﺘﻮر‬‫ﻓﺎﻛﺘﻮر‬‫اﻧﺸﻌﺎب‬‫اﻧﺸﻌﺎب‬‫ﻣﻮﺛﺮ‬‫ﻣﻮﺛﺮ‬(EBF)(EBF):
‫اﮔﺮ‬‫ﺗﻌﺪاد‬‫ﮔﺮه‬‫ﻫﺎي‬‫ﮔﺴﺘﺮش‬‫ﻳﺎﻓﺘﻪ‬‫ﺗﻮﺳﻂ‬‫روال‬‫ﺟﺴﺘﺠﻮ‬N‫ﺑﺎﺷﺪ‬‫و‬‫ﻋﻤﻖ‬‫راه‬‫ﺣﻞ‬d،‫ﺑﺎﺷﺪ‬
b*
‫ﻞ‬
b*‫ﺑﻪ‬‫ﺻﻮرت‬‫زﻳﺮ‬‫ﻣﺤﺎﺳﺒﻪ‬‫ﻣﻲ‬‫ﺷﻮد‬:
N + 1 = 1 + b* + (b*)2 + … + (b*)d
•‫ﻣﺜﺎل‬‫ﻣﺜﺎل‬::‫اﮔﺮ‬A*‫راه‬‫ﺣﻠﻲ‬‫را‬‫در‬‫ﻋﻤﻖ‬5‫ﺑﺎ‬‫اﺳﺘﻔﺎده‬‫از‬52‫ﮔﺮه‬‫ﭘﻴﺪا‬،‫ﻛﻨﺪ‬‫ﻓﺎﻛﺘﻮر‬‫اﻧﺸﻌﺎب‬‫ﻣﻮﺛﺮ‬
‫را‬‫ﻪ‬ ‫ﻣﺤﺎﺳ‬‫ﺪ‬ ‫ﻛﻨ‬ ‫را‬‫ﻣﺤﺎﺳﺒﻪ‬‫ﻛﻨﻴﺪ‬.
‫ﭘﺎﺳﺦ‬:
53 = 1 + b* + (b*)2 + (b*)5 b* = 1 9253 = 1 + b + (b ) + … (b ) b = 1.92
•‫در‬‫ﻳﻚ‬‫ﻫﻴﻮرﻳﺴﺘﻴﻚ‬‫ﻫﺮ‬‫ﭼﻪ‬‫ﻓﺎﻛﺘﻮر‬‫اﻧﺸﻌﺎب‬‫ﺑﻪ‬‫ﻳﻚ‬‫ﻧﺰدﻳﻜﺘﺮ‬،‫ﺑﺎﺷﺪ‬‫ﺑﻬﺘﺮ‬‫اﺳﺖ‬‫و‬‫آن‬ ‫ر‬‫ﻳ‬‫ﻴ‬ ‫ﻴﻮرﻳ‬‫ﺮ‬‫ﭼ‬‫ﻮر‬‫ب‬‫ﺑ‬‫ﻳ‬‫ﺮ‬ ‫ﻳ‬ ‫ﺰ‬‫ﺑ‬‫ﺮ‬ ‫ﺑﻬ‬‫و‬‫ن‬
‫ﻫﻴﻮرﻳﺴﺘﻴﻚ‬‫ﻛﻴﻔﻴﺖ‬‫ﻛﻴﻔﻴﺖ‬‫ﻛﺸﻒ‬‫ﻛﺸﻒ‬‫ﻛﻨﻨﺪﮔﻲ‬‫ﻛﻨﻨﺪﮔﻲ‬‫ﺑﻴﺸﺘﺮي‬‫دارد‬.
‫ﻣﻮﺛﺮ‬ ‫اﻧﺸﻌﺎب‬ ‫ﻓﺎﻛﺘﻮر‬ ‫و‬ ‫ﺟﺴﺘﺠﻮ‬ ‫ﻫﺰﻳﻨﻪ‬ ‫ﺑﻴﻦ‬ ‫ﻣﻘﺎﻳﺴﻪ‬‫ﻣﻮﺛﺮ‬ ‫اﻧﺸﻌﺎب‬ ‫ﻓﺎﻛﺘﻮر‬ ‫و‬ ‫ﺟﺴﺘﺠﻮ‬ ‫ﻫﺰﻳﻨﻪ‬ ‫ﺑﻴﻦ‬ ‫ﻣﻘﺎﻳﺴﻪ‬
S h C t Effective Branching FactorSearch Cost Effective Branching Factor
d IDS A*(h1) A*(h2) IDS A*(h1) A*(h2)
2 10 6 6 2.45 1.79 1.79
4 112 13 12 2.87 1.48 1.45
6 680 20 18 2 73 1 34 1 306 680 20 18 2.73 1.34 1.30
8 6,384 39 25 2.80 1.33 1.24
10 47,127 93 39 2.79 1.38 1.22
12 364,404 227 73 2.78 1.42 1.24
14 3,473,941 539 113 2.83 1.44 1.23
16 - 1,301 211 - 1.45 1.25
18 - 3,056 363 - 1.46 1.26
20 7 276 679 1 47 1 2720 - 7,276 679 - 1.47 1.27
22 - 18,094 1,219 - 1.48 1.28
24 - 39 135 1 641 - 1 48 1 26
24 39,135 1,641 1.48 1.26

‫ﻫﻴﻮرﻳﺴﺘﻴﻚ‬ ‫ﺗﻮاﺑﻊ‬ ‫اﺑﺪاع‬‫ﻫﻴﻮرﻳﺴﺘﻴﻚ‬ ‫ﺗﻮاﺑﻊ‬ ‫اﺑﺪاع‬
•‫ﻣﻲ‬‫ﺗﻮان‬‫ﻫﻴﻮرﻳﺴﺘﻴﻜﻬﺎي‬‫ﻗﺎﺑﻞ‬‫ﻗﺒﻮل‬‫را‬‫ﺑﺮاي‬‫ﻳﻚ‬،‫ﻣﺴﺄﻟﻪ‬‫از‬‫ﻫﺰﻳﻨﻪ‬‫دﻗﻴﻖ‬‫راه‬‫ﺣﻞ‬‫ﻳﻚ‬‫ﻧﺴﺨﻪ‬ ‫ﻞ‬‫ﻞ‬
‫راﺣﺖ‬‫راﺣﺖ‬(relaxed)(relaxed)‫از‬‫ﻣﺴﺄﻟﻪ‬‫ﺑﺪﺳﺖ‬‫آورد‬.
‫ﻚ‬AB‫ﮔ‬A •‫ﻣﺜﺎل‬:‫ﻗﺎﻧﻮن‬‫ﻣﻌﻤﺎي‬‫ﻫﺸﺖ‬.‫ﻳﻚ‬‫ﻛﺎﺷﻲ‬‫ﻣﻲ‬‫ﺗﻮاﻧﺪ‬‫از‬‫ﺧﺎﻧﻪ‬A‫ﺑﻪ‬‫ﺧﺎﻧﻪ‬B،‫ﺑﺮود‬‫اﮔﺮ‬A
‫ﻣﺠﺎور‬B‫ﺑﺎﺷﺪ‬‫و‬B‫ﺧﺎﻟﻲ‬‫ﺑﺎﺷﺪ‬.
••‫اﮔﺮ‬‫ﻗﻮاﻧﻴﻦ‬‫ﻣﻌﻤﺎي‬‫ﻫﺸﺖ‬‫ﺑﻪ‬‫ﮔﻮﻧﻪ‬‫اي‬‫راﺣﺖ‬‫ﺷﻮﻧﺪ‬‫ﻛﻪ‬‫ﻳﻚ‬‫ﻛﺎﺷﻲ‬‫ﺑﺘﻮاﻧﺪ‬‫ﺑﻪ‬‫ﺑﻪ‬‫ﻫﺮ‬‫ﻫﺮ‬‫ﺧﺎﻧﻪ‬‫ﺧﺎﻧﻪ‬‫اي‬‫اي‬
‫ﺣﺮﻛﺖ‬،‫ﻛﻨﺪ‬‫ﻫﻴﻮرﻳﺴﺘﻴﻚ‬h1(n)‫ﻛﻮﺗﺎﻫﺘﺮﻳﻦ‬‫راه‬‫ﺣﻞ‬‫را‬‫ﻣﻲ‬‫دﻫﺪ‬. ‫ﺖ‬ ‫ﺮ‬‫ﻴ‬ ‫ﻴﻮرﻳ‬1( )‫ﺮﻳﻦ‬ ‫ﻮ‬‫ر‬‫ﻞ‬‫ر‬‫ﻲ‬
••‫اﮔﺮ‬‫ﻗﻮاﻧﻴﻦ‬‫ﺑﻪ‬‫ﮔﻮﻧﻪ‬‫اي‬‫راﺣﺖ‬‫ﺷﻮﻧﺪ‬‫ﻛﻪ‬‫ﻳﻚ‬‫ﻛﺎﺷﻲ‬‫ﺑﺘﻮاﻧﺪ‬‫ﺑﻪ‬‫ﺑﻪ‬‫ﻫﺮ‬‫ﻫﺮ‬‫ﺧﺎﻧﻪ‬‫ﺧﺎﻧﻪ‬‫ﻣﺠﺎور‬‫ﻣﺠﺎور‬‫ﺣﺮﻛﺖ‬ ‫ﺮ‬‫ﻴﻦ‬ ‫ﻮ‬‫ﺑ‬‫ﻮ‬‫ﻮ‬‫ﻳ‬‫ﻲ‬‫ﻮ‬ ‫ﺑ‬‫ﺑ‬‫ﺑ‬‫ﺮ‬‫ﺮ‬‫ﺮ‬
،‫ﻛﻨﺪ‬‫ﻫﻴﻮرﻳﺴﺘﻴﻚ‬h2(n)‫ﻛﻮﺗﺎﻫﺘﺮﻳﻦ‬‫راه‬‫ﺣﻞ‬‫را‬‫ﻣﻲ‬‫دﻫﺪ‬.
‫ﻜ‬‫ﻜ‬‫ﻚ‬‫ﺄ‬ •‫ﻧﻜﺘﻪ‬‫ﻧﻜﺘﻪ‬‫ﻛﻠﻴﺪي‬‫ﻛﻠﻴﺪي‬::‫ﻫﺰﻳﻨﻪ‬‫راه‬‫ﺣﻞ‬‫ﺑﻬﻴﻨﻪ‬‫ﻳﻚ‬‫ﻣﺴﺄﻟﻪ‬،‫راﺣﺖ‬‫ﺑﻴﺸﺘﺮ‬‫از‬‫ﻫﺰﻳﻨﻪ‬‫راه‬‫ﺣﻞ‬‫ﺑﻬﻴﻨﻪ‬‫در‬
‫ﻣﺴﺄﻟﻪ‬‫واﻗﻌﻲ‬‫ﻧﻴﺴﺖ‬.
‫ﺳﺎزي‬ ‫ﺑﻬﻴﻨﻪ‬ ‫ﻣﺴﺎﺋﻞ‬ ‫و‬ ‫ﻣﺤﻠﻲ‬ ‫ﺟﺴﺘﺠﻮي‬ ‫ﻫﺎي‬ ‫اﻟﮕﻮرﻳﺘﻢ‬‫ﺳﺎزي‬ ‫ﺑﻬﻴﻨﻪ‬ ‫ﻣﺴﺎﺋﻞ‬ ‫و‬ ‫ﻣﺤﻠﻲ‬ ‫ﺟﺴﺘﺠﻮي‬ ‫ﻫﺎي‬ ‫اﻟﮕﻮرﻳﺘﻢ‬
••‫در‬‫ﺑﺴﻴﺎري‬‫از‬‫ﻣﺴﺎﺋﻞ‬‫ﺑﻬﻴﻨﻪ‬،‫ﺳﺎزي‬‫ﻣﺴﻴﺮ‬‫ﻣﺴﻴﺮ‬‫راه‬‫ﺣﻞ‬‫اﻫﻤﻴﺖ‬‫ﻧﺪارد؛‬‫ﺧﻮد‬‫ﺣﺎﻟﺖ‬
‫ﻫﺪف‬‫ﭘﺎﺳﺦ‬‫ﻣﺴﺄﻟﻪ‬‫ﻣﻲ‬‫ﺑﺎﺷﺪ‬. ‫ﺦ‬
•‫ﻓﻀﺎي‬‫ﺣﺎﻟﺖ‬=‫ﻣﺠﻤﻮﻋﻪ‬‫ﭘﻴﻜﺮه‬‫ﺑﻨﺪي‬‫ﻫﺎي‬»‫ﻛﺎﻣﻞ‬«‫؛‬ ‫ي‬ ‫ﻀ‬‫ﻮ‬ ‫ﺠ‬‫ﺮ‬ ‫ﭘﻴ‬‫ي‬ ‫ﺑ‬‫ي‬‫ﻞ‬
•‫ﻳﺎﻓﺘﻦ‬‫ﭘﻴﻜﺮه‬‫ﺑﻨﺪي‬،‫ﺑﻬﻴﻨﻪ‬‫ﻣﺎﻧﻨﺪ‬TSP
•‫ﻳﺎﻓﺘﻦ‬‫ﻳﻚ‬‫ه‬ ‫ﻜ‬ ‫ﭘ‬‫ﺑﻨﺪي‬‫ﻛﻪ‬‫ﻣﺤﺪودﻳﺖ‬‫ﻫﺎي‬‫ﺄﻟﻪ‬ ‫ﻣ‬‫را‬‫ارﺿﺎء‬،‫ﻛﻨﺪ‬‫ﻣﺎﻧﻨﺪ‬‫ﺎﻟﻪ‬ ‫ﻣ‬ •‫ﻳﺎﻓﺘﻦ‬‫ﻳﻚ‬‫ﭘﻴﻜﺮه‬‫ﺑﻨﺪي‬‫ﻛﻪ‬‫ﻣﺤﺪودﻳﺖ‬‫ﻫﺎي‬‫ﻣﺴﺄﻟﻪ‬‫را‬‫ارﺿﺎء‬،‫ﻛﻨﺪ‬‫ﻣﺎﻧﻨﺪ‬‫ﻣﺴﺎﻟﻪ‬
n-‫وزﻳﺮ‬
••‫در‬‫ﭼﻨﻴﻦ‬‫ﻣﻮاردي‬‫ﻣﻲ‬‫ﺗﻮان‬‫از‬‫اﻟﮕﻮرﻳﺘﻢ‬‫اﻟﮕﻮرﻳﺘﻢ‬‫ﻫﺎي‬‫ﻫﺎي‬‫ﺟﺴﺘﺠﻮي‬‫ﺟﺴﺘﺠﻮي‬‫ﻣﺤﻠﻲ‬‫ﻣﺤﻠﻲ‬‫ﺑﻬﺮه‬‫ﮔﺮﻓﺖ‬.
‫ﻚ‬‫ﻚ‬‫ﻟ‬‫ﻟ‬‫ﻈ‬‫ﻈ‬‫ﮕ‬‫ﮕ‬‫ﻛ‬‫ﻛ‬‫آ‬‫آ‬ ••‫ﻳﻚ‬‫ﻳﻚ‬‫ﺣﺎﻟﺖ‬‫ﺣﺎﻟﺖ‬»»‫ﻓﻌﻠﻲ‬‫ﻓﻌﻠﻲ‬««‫را‬‫را‬‫ﺑﻪ‬‫ﺑﻪ‬‫ﺗﻨﻬﺎﻳﻲ‬‫ﺗﻨﻬﺎﻳﻲ‬‫در‬‫در‬‫ﻧﻈﺮ‬‫ﻧﻈﺮ‬‫ﺑﮕﻴﺮ؛‬‫ﺑﮕﻴﺮ؛‬‫ﺳﻌﻲ‬‫ﺳﻌﻲ‬‫ﻛﻦ‬‫ﻛﻦ‬‫آن‬‫آن‬‫را‬‫را‬‫ﺑﻬﺒﻮد‬‫ﺑﻬﺒﻮد‬‫ﺑﺨﺸﻲ‬‫ﺑﺨﺸﻲ‬..
‫ﺳﺎزي‬ ‫ﺑﻬﻴﻨﻪ‬ ‫ﻣﺴﺎﺋﻞ‬ ‫و‬ ‫ﻣﺤﻠﻲ‬ ‫ﺟﺴﺘﺠﻮي‬ ‫ﻫﺎي‬ ‫اﻟﮕﻮرﻳﺘﻢ‬‫ﺳﺎزي‬ ‫ﺑﻬﻴﻨﻪ‬ ‫ﻣﺴﺎﺋﻞ‬ ‫و‬ ‫ﻣﺤﻠﻲ‬ ‫ﺟﺴﺘﺠﻮي‬ ‫ﻫﺎي‬ ‫اﻟﮕﻮرﻳﺘﻢ‬
•‫ﺟﺴﺘﺠﻮي‬‫ﺟﺴﺘﺠﻮي‬‫ﻣﺤﻠﻲ‬‫ﻣﺤﻠﻲ‬==‫اﺳﺘﻔﺎده‬‫از‬‫ﻳﻚ‬‫ﺣﺎﻟﺖ‬‫ﻓﻌﻠﻲ‬‫و‬‫ﺣﺮﻛﺖ‬‫ﺑﻪ‬‫ﺣﺎﻟﺖ‬‫ﻫﺎي‬
‫ﺎﻳﻪ‬ ‫ﻫ‬‫ﻫﻤﺴﺎﻳﻪ‬
•‫ﻣﺰاﻳﺎ‬:
–‫اﺳﺘﻔﺎده‬‫از‬‫ﺣﺎﻓﻈﻪ‬‫ﺑﺴﻴﺎر‬‫ﻛﻢ‬ ‫اﺳﺘﻔﺎده‬‫از‬‫ﺣﺎﻓﻈﻪ‬‫ﺑﺴﻴﺎر‬‫ﻛﻢ‬
–‫ﻳﺎﻓﺘﻦ‬‫راه‬‫ﺣﻞ‬‫ﻫﺎي‬‫ﻣﻌﻘﻮل‬‫در‬‫اﻏﻠﺐ‬‫ﻣﻮارد‬‫در‬‫ﻓﻀﺎﻫﺎي‬‫ﺣﺎﻟﺖ‬‫ﺑﺰرگ‬‫و‬‫ﻳﺎ‬‫ﻧﺎﻣﺤﺪود‬
•‫ﻣﻔﻴﺪ‬‫ﺑﺮاي‬‫ﻣﺴﺎﺋﻞ‬‫ﺑﻬﻴﻨﻪ‬‫ﺳﺎزي‬‫ﻣﺤﺾ‬
–‫ﻳﺎﻓﺘﻦ‬‫ﺑﻬﺘﺮﻳﻦ‬‫ﺣﺎﻟﺖ‬‫ﺑﺮ‬‫ﻃﺒﻖ‬‫ﺗﺎﺑﻊ‬‫ﻫﺪف‬(objective function)
‫ﻣﺜﺎل‬n‫وزﻳﺮ‬ ‫ﻣﺜﺎل‬:n-‫وزﻳﺮ‬
‫ﺄ‬‫ﻚ‬*‫ﮔ‬‫ﻛ‬ •‫ﻣﺴﺄﻟﻪ‬:n‫وزﻳﺮ‬‫را‬‫در‬‫ﻳﻚ‬‫ﺻﻔﺤﻪ‬‫ﺷﻄﺮﻧﺞ‬n * n‫ﺑﻪ‬‫ﮔﻮﻧﻪ‬‫اي‬‫ﻗﺮار‬‫ﺑﺪه‬‫ﻛﻪ‬
‫ﻫﻴﭻ‬‫دو‬‫وزﻳﺮي‬‫در‬‫ﻳﻚ‬،‫ﺳﻄﺮ‬‫ﺳﺘﻮن‬‫و‬‫ﻳﺎ‬‫ﻗﻄﺮ‬‫ﻗﺮار‬‫ﻧﮕﻴﺮﻧﺪ‬. ‫ﭻ‬
•‫ﻜ‬‫از‬‫ﻫﺎ‬ ‫ز‬‫ا‬‫د‬‫ن‬ ‫ﺘ‬‫دش‬ ‫ﺧ‬‫ﻪ‬‫ﻧﻪ‬ ‫ﮔ‬‫اي‬‫ﻪ‬‫ﺎ‬‫ﺎ‬‫ﻛ‬‫ﻛﻪ‬‫ﺪاد‬ ‫ﺗ‬ •‫ﻳﻜﻲ‬‫از‬‫وزﻳﺮﻫﺎ‬‫را‬‫در‬‫ﺳﺘﻮن‬‫ﺧﻮدش‬‫ﺑﻪ‬‫ﮔﻮﻧﻪ‬‫اي‬‫ﺟﺎﺑﻪ‬‫ﺟﺎ‬‫ﻛﻦ‬‫ﻛﻪ‬‫ﺗﻌﺪاد‬
‫ﺑﺮﺧﻮرد‬‫ﻫﺎ‬‫ﻛﺎﻫﺶ‬‫ﻳﺎﺑﺪ‬.

‫ﻧﻮردي‬ ‫ﺗﭙﻪ‬)‫ﺻﻌﻮدي‬ ‫ﮔﺮادﻳﺎن‬ ‫ﻳﺎ‬/‫ﻧﺰوﻟﻲ‬( ‫ﻧﻮردي‬ ‫ﺗﭙﻪ‬)‫ﺻﻌﻮدي‬ ‫ﮔﺮادﻳﺎن‬ ‫ﻳﺎ‬/‫ﻧﺰوﻟﻲ‬(
»‫ﺣﺎﻓﻈﻪ‬ ‫ﺿﻌﻒ‬ ‫ﺑﺎ‬ ‫ﻏﻠﻴﻆ‬ ‫ﻣﻪ‬ ‫در‬ ‫اورﺳﺖ‬ ‫ﻛﻮه‬ ‫از‬ ‫رﻓﺘﻦ‬ ‫ﺑﺎﻻ‬ ‫ﻣﺎﻧﻨﺪ‬«
‫ﻣﺜﺎل‬‫ﻧﻮردي‬ ‫ﺗﭙﻪ‬ ‫ﺟﺴﺘﺠﻮي‬ ‫ﻣﺜﺎل‬:‫ﻧﻮردي‬ ‫ﺗﭙﻪ‬ ‫ﺟﺴﺘﺠﻮي‬
S
A D
G
h = 5 h = 2
‫ﺳﺮاﺳﺮي‬ ‫ﺑﻬﻴﻨﻪ‬
‫ﺷﺮوع‬ ‫ﺣﺎﻟﺖ‬
B E
h = 7
h = 4
h = 2
h = 0
F
C H
h = 1
h = 5 h = 4
S
A D
G
h = 5
‫ﻓﻌﻠﻲ‬ ‫ﺣﺎﻟﺖ‬
B E
h = 7
h = 4
B
‫ﻫﻤﺴﺎﻳﻪ‬ ‫ﺑﻬﺘﺮﻳﻦ‬
F
C H
‫ﻫﻤﺴﺎﻳﻪ‬ ‫ﺣﺎﻟﺖ‬
S
A D
G
h = 5
B E
h = 7
h = 4
h = 2
E
F
C H
h = 5

S
A D
G
h = 2
B E
h = 4
h = 2
F
C H
h = 1
F ‫ﻫﻤﺴﺎﻳﻪ‬ ‫ﺣﺎﻟﺖ‬
S
A D
G ‫ﻓﻌﻠﻲ‬ ‫ﺣﺎﻟﺖ‬
B E
h = 2
E
F
C H
h = 1
S
A D
G
h = 5 h = 2
B E
h = 7
h = 4
h = 2
h = 0
F
C H
h = 1
h = 5 h = 4
‫ﻣﺤﻠﻲ‬ ‫ﻣﻴﻨﻴﻤﻢ‬
‫ﻧﻮردي‬ ‫ﺗﭙﻪ‬ ‫ﺟﺴﺘﻮي‬:‫ﻣﺜﺎل‬8‫وزﻳﺮ‬ ‫ﻧﻮردي‬ ‫ﺗﭙﻪ‬ ‫ﺟﺴﺘﻮي‬:‫ﻣﺜﺎل‬8‫وزﻳﺮ‬
5‫ﺣﺮﻛﺖ‬‫ﺮ‬
h = 17 h = 1h = 17 h = 1
h=‫ﻛﻨﻨﺪ‬ ‫ﻣﻲ‬ ‫ﺗﻬﺪﻳﺪ‬ ‫را‬ ‫ﻳﻜﺮﻳﮕﺮ‬ ‫ﻣﺴﺘﻘﻴﻢ‬ ‫ﻏﻴﺮ‬ ‫ﺑﻄﻮر‬ ‫ﻳﺎ‬ ‫و‬ ‫ﻣﺴﺘﻘﻴﻢ‬ ‫ﺑﻄﻮر‬ ‫ﻛﻪ‬ ‫وزﻳﺮﻫﺎﻳﻲ‬ ‫ﺟﻔﺖ‬ ‫ﺗﻌﺪاد‬.

‫ﻧﻮردي‬ ‫ﺗﭙﻪ‬)‫اداﻣﻪ‬( ‫ﻧﻮردي‬ ‫ﺗﭙﻪ‬)‫اداﻣﻪ‬(
••‫ﻣﺸﻜﻞ‬:‫ﺑﺴﺘﻪ‬‫ﺑﻪ‬‫ﺣﺎﻟﺖ‬‫اوﻟﻴﻪ‬‫ﻣﺴﺄﻟﻪ‬‫ﻣﻤﻜﻦ‬‫اﺳﺖ‬‫در‬‫ﻣﺎﻛﺰﻳﻤﻢ‬‫ﻣﺎﻛﺰﻳﻤﻢ‬‫ﻣﺤﻠﻲ‬‫ﻣﺤﻠﻲ‬‫ﮔﻴﺮ‬ ‫ﻢ‬‫ﻢ‬
‫ﻛﻨﺪ‬.
‫ﻧﻮردي‬ ‫ﺗﭙﻪ‬ ‫دﻳﮕﺮ‬ ‫اﻧﻮاع‬)‫اﺗﻔﺎﻗ‬ ‫ﻧﻮردي‬ ‫ﺗﭙﻪ‬( ‫ﻧﻮردي‬ ‫ﺗﭙﻪ‬ ‫دﻳﮕﺮ‬ ‫اﻧﻮاع‬)‫اﺗﻔﺎﻗﻲ‬ ‫ﻧﻮردي‬ ‫ﺗﭙﻪ‬(
‫ﻔﺎﻗ‬ ‫ا‬ •‫ﺗﭙﻪ‬‫ﻧﻮردي‬‫اﺗﻔﺎﻗﻲ‬
–‫اﻧﺘﺨﺎب‬‫ﺗﺼﺎدﻓﻲ‬‫در‬‫ﻣﻴﺎن‬‫ﺣﺮﻛﺖ‬‫ﻫﺎي‬‫رو‬‫ﺑﻪ‬‫ﺑﺎﻻ‬
–‫اﺣﺘﻤﺎل‬‫اﻧﺘﺨﺎب‬‫ﻣﻲ‬‫ﺗﻮاﻧﺪ‬‫ﻣﺘﻨﺎﺳﺐ‬‫ﺑﺎ‬‫ﺷﻴﺐ‬‫ﺣﺮﻛﺖ‬‫ﺗﻐﻴﻴﺮ‬‫ﻛﻨﺪ‬
A D
h = 5 h = 29
4 A D
h = 5 h = 2
3
2
0
S
B E
G
h = 7
h 4
h = 0
9
5
S
B E
G
h = 7
h 4
h = 0
3
1
F
h = 4
h = 2
h = 1
F
h = 4
h = 2
h = 1
C H
h = 5 h = 4
C H
h = 5 h = 4
‫ﻧﻮردي‬ ‫ﺗﭙﻪ‬ ‫دﻳﮕﺮ‬ ‫اﻧﻮاع‬)‫اﺗﻔﺎﻗ‬ ‫ﻧﻮردي‬ ‫ﺗﭙﻪ‬( ‫ﻧﻮردي‬ ‫ﺗﭙﻪ‬ ‫دﻳﮕﺮ‬ ‫اﻧﻮاع‬)‫اﺗﻔﺎﻗﻲ‬ ‫ﻧﻮردي‬ ‫ﺗﭙﻪ‬(
‫ﻔﺎﻗ‬ ‫ا‬ •‫ﺗﭙﻪ‬‫ﻧﻮردي‬‫اﺗﻔﺎﻗﻲ‬
–‫اﻧﺘﺨﺎب‬‫ﺗﺼﺎدﻓﻲ‬‫در‬‫ﻣﻴﺎن‬‫ﺣﺮﻛﺖ‬‫ﻫﺎي‬‫رو‬‫ﺑﻪ‬‫ﺑﺎﻻ‬
–‫اﺣﺘﻤﺎل‬‫اﻧﺘﺨﺎب‬‫ﻣﻲ‬‫ﺗﻮاﻧﺪ‬‫ﻣﺘﻨﺎﺳﺐ‬‫ﺑﺎ‬‫ﺷﻴﺐ‬‫ﺣﺮﻛﺖ‬‫ﺗﻐﻴﻴﺮ‬‫ﻛﻨﺪ‬
A D
h = 5 h = 2
A D
h = 5 h = 2 0
0
1
S
B E
G
h = 7
h 4
h = 0
S
B E
G
h = 7
h 4
h = 0
0
F
h = 4
h = 2
h = 1
F
h = 4
h = 2
h = 1
C H
h = 5 h = 4
C H
h = 5 h = 4
‫ﻧﻮردي‬ ‫ﺗﭙﻪ‬ ‫دﻳﮕﺮ‬ ‫اﻧﻮاع‬)‫اﻧﺘﺨﺎب‬ ‫اوﻟﻴﻦ‬ ‫ﻧﻮردي‬ ‫ﺗﭙﻪ‬( ‫ﻧﻮردي‬ ‫ﺗﭙﻪ‬ ‫دﻳﮕﺮ‬ ‫اﻧﻮاع‬)‫اﻧﺘﺨﺎب‬ ‫اوﻟﻴﻦ‬ ‫ﻧﻮردي‬ ‫ﺗﭙﻪ‬(
‫ﻟ‬ ‫ا‬‫ﺎ‬ ‫ا‬ •‫ﺗﭙﻪ‬‫ﻧﻮردي‬‫اوﻟﻴﻦ‬‫اﻧﺘﺨﺎب‬
–‫ﻫﻤﺎن‬‫ﺗﭙﻪ‬‫ﻧﻮردي‬‫اﺗﻔﺎﻗﻲ‬‫ﻛﻪ‬‫ﺣﺎﻟﺖ‬‫ﻫﺎي‬‫ﺑﻌﺪي‬‫را‬‫ﺑﻪ‬‫ﻃﻮر‬‫ﺗﺼﺎدﻓﻲ‬‫ﺗﻮﻟﻴﺪ‬‫ﻣﻲ‬‫ﻛﻨﺪ‬‫ﺗﺎ‬
‫ﻳﻜﻲ‬‫از‬‫آﻧﻬﺎ‬‫ﺑﻬﺘﺮ‬‫از‬‫ﺣﺎﻟﺖ‬‫ﻓﻌﻠﻲ‬‫ﺑﺎﺷﺪ‬
S(7) A(5)
S
A D
G
h = 5 h = 2
S(7)
S(7) A(5)
S
B E
G
h = 7
h = 4
h = 2
h = 0 A(5) B(4)
C(5)
S(7)
F
C H
h = 1 B(4)
C(5)
C
h = 5 h = 4
E(2)
E(2) F(1)

‫ﻧ‬ ‫ﺗ‬ ‫ﮕ‬ ‫ا‬ ‫اﻧ‬)‫ﻓ‬ ‫ﺎ‬ ‫ﺗ‬ ‫ﺷ‬ ‫ﺎ‬ ‫ﻧ‬ ‫ﺗ‬( ‫ﻧﻮردي‬ ‫ﺗﭙﻪ‬ ‫دﻳﮕﺮ‬ ‫اﻧﻮاع‬)‫ﺗﺼﺎدﻓﻲ‬ ‫ﻣﺠﺪد‬ ‫ﺷﺮوع‬ ‫ﺑﺎ‬ ‫ﻧﻮردي‬ ‫ﺗﭙﻪ‬(
‫ﺎ‬‫ﺷ‬‫ﻓ‬ ‫ﺎ‬)‫ا‬‫اﮔ‬‫ﺷﻜ‬‫ﺎ‬‫ﻛ‬( •‫ﺗﭙﻪ‬‫ﻧﻮردي‬‫ﺑﺎ‬‫ﺷﺮوع‬‫ﻣﺠﺪد‬‫ﺗﺼﺎدﻓﻲ‬)‫اﻳﺪه‬:‫اﮔﺮ‬‫ﺷﻜﺴﺖ‬‫ﺧﻮردي‬‫دوﺑﺎره‬‫ﺳﻌﻲ‬‫ﻛﻦ‬(
–‫ﺳﻌﻲ‬‫ﻣﻲ‬‫ﻛﻨﺪ‬‫ﻛﻪ‬‫از‬‫ﮔﻴﺮ‬‫اﻓﺘﺎدن‬‫در‬‫ﻣﺎﻛﺰﻳﻤﻢ‬‫ﻣﺤﻠﻲ‬‫اﺟﺘﻨﺎب‬‫ﻛﻨﺪ‬
A D
h = 5 h = 2 S B E F
S
B E
G
h = 7
h = 4
h = 0
C B E F
H
F
h = 4
h = 2
h = 1
C H
C H
h = 5 h = 4
A D G
‫ﻜﻞ‬‫ا‬ ‫ا‬ ‫ا‬ ‫ﺄﻟ‬ ‫ﻞ‬ ‫ا‬ ‫ﻞ‬ ‫ا‬ ‫ﺎ‬ ‫ﺄﻟ‬ ‫ﻚ‬ ً‫ﻻ‬ ‫ﻣﺸﻜﻞ‬:‫داﻧﻴﻢ‬ ‫ﻧﻤﻲ‬ ‫را‬ ‫ﺑﻬﻴﻨﻪ‬ ‫ﺟﻮاب‬ ‫ﻣﺴﺄﻟﻪ‬ ‫ﺣﻞ‬ ‫از‬ ‫ﻗﺒﻞ‬ ،‫واﻗﻌﻲ‬ ‫دﻧﻴﺎي‬ ‫در‬ ‫ﻣﺴﺄﻟﻪ‬ ‫ﻳﻚ‬ ‫در‬ ‫ﻣﻌﻤﻮﻻ‬!
‫ﺣﻞ‬ ‫راه‬:‫دارﻳﻢ‬ ‫وﻗﺖ‬ ‫ﻛﻪ‬ ‫زﻣﺎﻧﻲ‬ ‫ﺗﺎ‬ ‫ﻧﻮردي‬ ‫ﺗﭙﻪ‬ ‫ﺗﻜﺮار‬.
‫ﻧ‬ ‫ﺗ‬ ‫ﮕ‬ ‫ا‬ ‫اﻧ‬)‫ﻓ‬ ‫ﺎ‬ ‫ﺗ‬ ‫ﺷ‬ ‫ﺎ‬ ‫ﻧ‬ ‫ﺗ‬( ‫ﻧﻮردي‬ ‫ﺗﭙﻪ‬ ‫دﻳﮕﺮ‬ ‫اﻧﻮاع‬)‫ﺗﺼﺎدﻓﻲ‬ ‫ﻣﺠﺪد‬ ‫ﺷﺮوع‬ ‫ﺑﺎ‬ ‫ﻧﻮردي‬ ‫ﺗﭙﻪ‬(
‫ﺎ‬‫ا‬‫ﻂ‬‫ﻞ‬ ‫ا‬‫ﺎ‬‫ﺷ‬‫ﻓ‬ ‫ﺎ‬ •‫ﻣﺤﺎﺳﺒﻪ‬‫ﺗﻌﺪاد‬‫ﻣﺘﻮﺳﻂ‬‫ﻣﺮاﺣﻞ‬‫در‬‫ﺗﭙﻪ‬‫ﻧﻮردي‬‫ﺑﺎ‬‫ﺷﺮوع‬‫ﺗﺼﺎدﻓﻲ‬‫ﻣﺠﺪد‬
–‫ﺗﻌﺪاد‬‫ﺗﻜﺮارﻫﺎ‬=‫ﻋﻜﺲ‬‫اﺣﺘﻤﺎل‬‫ﻣﻮﻓﻘﻴﺖ‬
‫ﻂ‬‫ﻜ‬‫ﻂ‬‫ﻜ‬‫ﻂ‬ –‫ﺗﻌﺪاد‬‫ﻣﺘﻮﺳﻂ‬‫ﻣﺮاﺣﻞ‬=‫ﺗﻌﺪاد‬‫ﺷﻜﺴﺖ‬‫ﻫﺎ‬*‫ﺗﻌﺪاد‬‫ﻣﺘﻮﺳﻂ‬‫ﻣﺮاﺣﻞ‬‫ﺷﻜﺴﺖ‬+1*‫ﺗﻌﺪاد‬‫ﻣﺘﻮﺳﻂ‬
‫ﻣﺮاﺣﻞ‬‫ﻣﻮﻓﻘﻴﺖ‬
S(3)
A(2) D(1)
G(0)
h = 5 h = 2
‫ﻣﻮﻓﻘﻴﺖ‬ ‫اﺣﺘﻤﺎل‬=
3
1
‫ﺗﻜﺮارﻫﺎ‬ ‫ﺗﻌﺪاد‬3
B(2) E(1)
h = 7
h = 4
h = 2
h = 0 ‫ﺗﻜﺮارﻫﺎ‬ ‫ﺗﻌﺪاد‬=3
‫ﻣﻮﻓﻘﻴﺖ‬ ‫ﻣﺮاﺣﻞ‬ ‫ﻣﺘﻮﺳﻂ‬ ‫ﺗﻌﺪاد‬=1
‫ﺷﻜ‬ ‫ﻞ‬ ‫ا‬ ‫ﻂ‬ ‫ﺘ‬ ‫ا‬ ‫ﺗ‬8F(0)
C(2) H(0)
h h
h = 1
‫ﺷﻜﺴﺖ‬ ‫ﻣﺮاﺣﻞ‬ ‫ﻣﺘﻮﺳﻂ‬ ‫ﺗﻌﺪاد‬=6
8
( ) 411813 ≅×+×−h = 5 h = 4 ( ) 411
6
13 ≅×+×−
Simulated Annealingg
•‫اﻳﺪه‬:‫از‬‫ﻣﺎﻛﺰﻳﻤﻢ‬‫ﻫﺎي‬‫ﻣﺤﻠﻲ‬‫ﺑﺎ‬‫اﻧﺠﺎم‬‫ﺣﺮﻛﺖ‬‫ﻫﺎي‬»‫ﺑﺪ‬«‫ﻓﺮار‬‫ﻛﻦ‬
‫ﺎ‬ ‫ا‬‫ﺎ‬ ‫ا‬‫ﻪ‬‫ﻪ‬‫ﺗﺪ‬‫ﺗﺪ‬‫اﻧﺪاز‬‫اﻧﺪاز‬‫ﺪاد‬ ‫ﺗ‬‫ﺪاد‬ ‫ﺗ‬‫ﻛﺎت‬‫ﻛﺎت‬‫ﺪ‬‫ﺪ‬))‫ﻪ‬‫ﻪ‬‫ﺖ‬‫ﺖ‬‫ﻦ‬ ‫ﺎ‬‫ﻦ‬ ‫ﺎ‬((‫ا‬‫ا‬‫ﻛ‬‫ﻛ‬‫ﻛﻦ‬‫ﻛﻦ‬ ‫اﻣﺎ‬‫اﻣﺎ‬‫ﺑﻪ‬‫ﺑﻪ‬‫ﺗﺪرﻳﺞ‬‫ﺗﺪرﻳﺞ‬‫اﻧﺪازه‬‫اﻧﺪازه‬‫و‬‫و‬‫ﺗﻌﺪاد‬‫ﺗﻌﺪاد‬‫ﺣﺮﻛﺎت‬‫ﺣﺮﻛﺎت‬‫ﺑﺪ‬‫ﺑﺪ‬))‫ﺑﻪ‬‫ﺑﻪ‬‫ﺳﻤﺖ‬‫ﺳﻤﺖ‬‫ﭘﺎﻳﻴﻦ‬‫ﭘﺎﻳﻴﻦ‬((‫را‬‫را‬‫ﻛﻢ‬‫ﻛﻢ‬‫ﻛﻦ‬‫ﻛﻦ‬
‫ﺷﺪه‬ ‫ﺳﺎزي‬ ‫ﺷﺒﻴﻪ‬ ‫آﻧﻠﻴﻨﮓ‬‫ﺷﺪه‬ ‫ﺳﺎزي‬ ‫ﺷﺒﻴﻪ‬ ‫آﻧﻠﻴﻨﮓ‬
••‫ﭘﻴﺸﺮوي‬‫ﻣﺎﻧﻨﺪ‬‫ﺗﭙﻪ‬‫ﻧﻮردي‬‫ﻣﻲ‬،‫ﺑﺎﺷﺪ‬‫اﻣﺎ‬‫در‬‫ﻫﺮ‬‫ﻣﺮﺣﻠﻪ‬‫ﺣﺎﻟﺖ‬‫ﺑﻌﺪي‬‫ﺑﻪ‬‫ﻃﻮر‬
‫ﺗﺼﺎدﻓ‬‫ﺗﺼﺎدﻓ‬‫اﻧﺘﺨﺎب‬‫ﻣ‬‫ﺷﻮد‬ ‫ﺗﺼﺎدﻓﻲ‬‫ﺗﺼﺎدﻓﻲ‬‫اﻧﺘﺨﺎب‬‫ﻣﻲ‬‫ﺷﻮد‬.
•‫اﮔﺮ‬‫ﺣﺎﻟﺖ‬‫ﺑﻌﺪي‬‫اﻧﺘﺨﺎب‬‫ﺷﺪه‬‫ﺑﻬﺘﺮ‬،‫ﺑﺎﺷﺪ‬‫ﻫﻤﻮاره‬‫ﺑﻪ‬‫آن‬‫ﺣﺎﻟﺖ‬‫ﺑﻌﺪي‬‫ﺧﻮاﻫﻴﻢ‬
‫رﻓﺖ‬.
•‫در‬‫ﻏﻴﺮ‬‫اﻳﻦ‬،‫ﺻﻮرت‬‫ﺗﻨﻬﺎ‬‫ﺑﺎ‬‫ﻳﻚ‬‫اﺣﺘﻤﺎل‬‫ﺑﻪ‬‫آن‬‫ﺣﺎﻟﺖ‬‫ﺧﻮاﻫﻴﻢ‬‫رﻓﺖ‬‫و‬‫اﻳﻦ‬ ‫ر‬‫ﻴﺮ‬‫ﻳﻦ‬‫ﻮر‬‫ﻬ‬‫ﺑ‬‫ﻳ‬‫ل‬‫ﺑ‬‫ﻴﻢ‬ ‫ﻮ‬‫ر‬‫و‬‫ﻳﻦ‬
‫اﺣﺘﻤﺎل‬‫ﺑﻪ‬‫ﺻﻮرت‬‫ﻧﻤﺎﻳﻲ‬‫ﻛﺎﻫﺶ‬‫ﻣﻲ‬‫ﻳﺎﺑﺪ‬.
•‫ﺗﺎ‬‫ﺎل‬ ‫ا‬ •‫ﺗﺎﺑﻊ‬‫اﺣﺘﻤﺎل‬:
T:‫ﺗﻌﺪاد‬‫ﻣﺮاﺣﻞ‬‫ﺑﻬﺒﻮد‬‫راه‬‫ﺣﻞ‬e ΔE/T
‫ﻞ‬ ‫ﺮ‬‫ﺒﻮ‬ ‫ﺑ‬‫ﻞ‬
ΔE:‫ﻣﻴﺰان‬‫ﻛﺎﻫﺶ‬‫در‬‫ﻫﺮ‬‫ﻣﺮﺣﻠﻪ‬

‫ﺷﺪه‬ ‫ﺳﺎزي‬ ‫ﺷﺒﻴﻪ‬ ‫آﻧﻠﻴﻨﮓ‬ ‫ﺧﺼﻮﺻﻴﺎت‬‫ﺷﺪه‬ ‫ﺳﺎزي‬ ‫ﺷﺒﻴﻪ‬ ‫آﻧﻠﻴﻨﮓ‬ ‫ﺧﺼﻮﺻﻴﺎت‬
•‫در‬‫ﻣﻘﺎدﻳﺮ‬‫ﺑﺎﻻﺗﺮ‬T،‫اﺣﺘﻤﺎل‬‫اﻧﺠﺎم‬‫ﺣﺮﻛﺎت‬‫ﺑﺪ‬)‫رﻓﺘﻦ‬‫ﺑﻪ‬‫ﺳﻤﺖ‬‫ﭘﺎﻳﻴﻦ‬(‫ﺑﻴﺸﺘﺮ‬‫اﺳﺖ‬.)‫ﻣﺎﻧﻨﺪ‬‫ﺟﺴﺘﺠﻮي‬‫ﺗﺼﺎدﻓﻲ‬
‫رﻓﺘﺎر‬‫ﻣﻲ‬‫ﻛﻨﺪ‬(
•‫ﺑﺎ‬‫ﻛﺎﻫﺶ‬T‫اﻳﻦ‬‫اﺣﺘﻤﺎل‬‫ﻛﺎﻫﺶ‬‫ﻳﺎﻓﺘﻪ‬‫و‬‫در‬T = 0‫اﻳﻦ‬‫اﺣﺘﻤﺎل‬‫ﺑﻪ‬‫ﺻﻔﺮ‬‫ﻣﻲ‬‫رﺳﺪ‬.)‫ﻣﺎﻧﻨﺪ‬‫ﺗﭙﻪ‬‫ﻧﻮردي‬‫رﻓﺘﺎر‬
‫ﻣﻲ‬‫ﻛﻨﺪ‬(
•‫ﻣﻲ‬‫ﺗﻮان‬‫ﺛﺎﺑﺖ‬‫ﻛﺮد‬‫ﻛﻪ‬‫اﮔﺮ‬T‫ﺑﻪ‬‫ﺑﻪ‬‫اﻧﺪازه‬‫ﻛﺎﻓﻲ‬‫آرام‬‫ﻛﺎﻫﺶ‬،‫ﺑﻴﺎﺑﺪ‬‫ﺟﺴﺘﺠﻮي‬SA‫ﻳﻚ‬‫ﭘﺎﺳﺦ‬‫ﺑﻬﻴﻨﻪ‬
(global optimum)‫ﺑﺎ‬‫اﺣﺘﻤﺎﻟﻲ‬‫ﻛﻪ‬‫ﺑﻪ‬‫ﺳﻤﺖ‬‫ﻳﻚ‬‫ﻣﻴﻞ‬‫ﻣﻲ‬‫ﻛﻨﺪ‬‫ﺧﻮاﻫﺪ‬‫ﻳﺎﻓﺖ‬. (g p )‫ﺑ‬‫ﻲ‬‫ﺑ‬‫ﻳ‬‫ﻴﻞ‬‫ﻲ‬‫ﻮ‬‫ﻳ‬
•‫ﻛﺎرﺑﺮدﻫﺎ‬:
‫ﻞ‬‫ﺎﺋﻞ‬VLSI –‫ﺣﻞ‬‫ﻣﺴﺎﺋﻞ‬VLSI
–‫ﺑﺮﻧﺎﻣﻪ‬‫رﻳﺰي‬
–‫اﻋﻤﺎل‬‫ﺑﻬﻴﻨﻪ‬‫ﺳﺎزي‬
–‫زﻣﺎﻧﺒﻨﺪي‬‫ﺧﻄﻮط‬‫ﻫﻮاﻳﻲ‬
Local beam search
)‫اي‬ ‫دﺳﺘﻪ‬ ‫ﻣﺤﻠﻲ‬ ‫ﺟﺴﺘﺠﻮي‬(
•‫ﺷﺮوع‬‫ﺑﺎ‬k‫ﺣﺎﻟﺖ‬‫ﻛﻪ‬‫ﺑﻪ‬‫ﻃﻮر‬‫ﺗﺼﺎدﻓﻲ‬‫اﻳﺠﺎد‬‫ﺷﺪه‬‫اﻧﺪ‬.
•‫در‬‫ﻫ‬،‫ار‬ ‫ﺗﻜ‬‫ﺎم‬ ‫ﺗ‬‫زﻧﺪان‬ ‫ﻓ‬‫اي‬ ‫ﺑ‬‫ﻫ‬k‫ﺣﺎﻟﺖ‬‫ﺪ‬ ‫ﻟ‬ ‫ﺗ‬‫ﻣ‬‫ﻧﺪ‬ ‫ﺷ‬ •‫در‬‫ﻫﺮ‬،‫ﺗﻜﺮار‬‫ﺗﻤﺎم‬‫ﻓﺮزﻧﺪان‬‫ﺑﺮاي‬‫ﻫﺮ‬k‫ﺣﺎﻟﺖ‬‫ﺗﻮﻟﻴﺪ‬‫ﻣﻲ‬‫ﺷﻮﻧﺪ‬.
•‫اﮔﺮ‬‫ﻳﻜﻲ‬‫از‬‫آﻧﻬﺎ‬‫ﺣﺎﻟﺖ‬‫ﻫﺪف‬‫ﺑﻮد‬‫ﺟﺴﺘﺠﻮ‬‫ﻣﺘﻮﻗﻒ‬‫ﻣﻲ‬‫ﺷﻮد‬‫و‬‫در‬
‫ﻟ‬‫ﻞ‬ ‫ﻛ‬k ‫ﻏﻴﺮ‬‫اﻳﻦ‬‫ﺻﻮرت‬‫از‬‫ﻣﻴﺎن‬‫ﻟﻴﺴﺖ‬‫ﻛﺎﻣﻞ‬‫ﻓﺮزﻧﺪان‬k‫ﺗﺎ‬‫از‬‫ﺑﻬﺘﺮﻳﻦ‬‫ﻫﺎ‬
‫اﻧﺘﺨﺎب‬‫ﻣﻲ‬‫ﺷﻮﻧﺪ‬‫و‬‫ﻣﺮﺣﻠﻪ‬‫ﺑﺎﻻ‬‫ﺗﻜﺮار‬‫ﻣﻲ‬‫ﺷﻮد‬. ‫ب‬‫ﻲ‬‫ﻮ‬‫و‬‫ﺮ‬‫ﺑ‬‫ر‬ ‫ﺮ‬‫ﻲ‬‫ﻮ‬
‫ژﻧﺘﻴﻚ‬ ‫اﻟﮕﻮرﻳﺘﻢ‬‫ژﻧﺘﻴﻚ‬ ‫اﻟﮕﻮرﻳﺘﻢ‬
•‫ﻧﻮﻋﻲ‬‫از‬local beam search‫ﺑﻪ‬‫ﻫﻤﺮاه‬‫ﺗﺮﻛﻴﺐ‬‫ﺟﻨﺴﻲ‬
‫ژﻧﺘﻴﻚ‬ ‫ﻫﺎي‬ ‫اﻟﮕﻮرﻳﺘﻢ‬‫ژﻧﺘﻴﻚ‬ ‫ﻫﺎي‬ ‫اﻟﮕﻮرﻳﺘﻢ‬
•‫ﻳﻚ‬‫ﺣﺎﻟﺖ‬‫ﺑﻌﺪي‬‫ﺑﺎ‬‫ﺗﺮﻛﻴﺐ‬‫دو‬‫ﺣﺎﻟﺖ‬‫ﭘﺪر‬‫اﻳﺠﺎد‬‫ﻣﻲ‬‫ﺷﻮد‬.
•‫وع‬ ‫ﺷ‬‫ﺑﺎ‬k‫ﺣﺎﻟﺖ‬‫ﺗﺼﺎدﻓ‬)‫ﺖ‬ ‫ﻌ‬ ‫ﺟ‬( •‫ﺷﺮوع‬‫ﺑﺎ‬k‫ﺣﺎﻟﺖ‬‫ﺗﺼﺎدﻓﻲ‬)‫ﺟﻤﻌﻴﺖ‬(
•‫ﻳﻚ‬‫ﺣﺎﻟﺖ‬‫ﺑﺎ‬‫ﻳﻚ‬‫رﺷﺘﻪ‬‫ﺑﺮ‬‫روي‬‫ﻳﻚ‬‫ﻣﺠﻤﻮﻋﻪ‬‫ﻣﺤﺪود‬‫ﺑﺎزﻧﻤﺎﻳﻲ‬‫ﻣﻲ‬‫ﺷﻮد‬
)‫اﻏﻠ‬‫ﺷ‬‫ا‬‫ا‬‫ﻔ‬‫ﻚ‬()‫ﻛ‬( )‫اﻏﻠﺐ‬‫رﺷﺘﻪ‬‫اي‬‫از‬‫ﺻﻔﺮ‬‫و‬‫ﻳﻚ‬(–)‫ﻛﺮوﻣﻮزوم‬(
•‫ﺗﺎﺑﻊ‬‫ارزﻳﺎﺑﻲ‬)‫ﺗﺎﺑﻊ‬‫ﺑﺮازﻧﺪﮔﻲ‬–Fitness function(:‫ﻣﻘﺎدﻳﺮ‬‫ﺑﺎﻻﺗﺮي‬ ‫ﻊ‬‫ﻊ‬
‫را‬‫ﺑﺮاي‬‫ﺣﺎﻻت‬‫ﺑﻬﺘﺮ‬‫اﻳﺠﺎد‬‫ﻣﻲ‬‫ﻛﻨﺪ‬.
•‫ﻧﺴﻞ‬‫ﺑﻌﺪي‬‫ﺟﻤﻌﻴﺖ‬‫ﺑﺎ‬‫اﻧﺠﺎم‬‫اﻋﻤﺎل‬‫زﻳﺮ‬‫روي‬‫ﺟﻤﻌﻴﺖ‬‫ﻓﻌﻠﻲ‬‫ﺗﻮﻟﻴﺪ‬‫ﻣﻲ‬‫ﺷﻮد‬: ‫ﻞ‬‫ي‬ ‫ﺑ‬‫ﻴ‬ ‫ﺟ‬‫ﺑ‬‫م‬ ‫ﺠ‬‫زﻳﺮ‬‫روي‬‫ﻴ‬ ‫ﺟ‬‫ﻲ‬‫ﻴ‬ ‫ﻮ‬‫ﻲ‬‫ﻮ‬
–‫اﻧﺘﺨﺎب‬(Selection)
–‫آﻣﻴﺰش‬(Crossover) ‫آﻣﻴﺰش‬(Crossover)
–‫ﺟﻬﺶ‬(Mutation)

•‫ﺗﺎﺑﻊ‬‫ﺑﺮازﻧﺪﮔﻲ‬:‫ﺗﻌﺪاد‬‫ﺟﻔﺖ‬‫وزﻳﺮ‬‫ﻫﺎﻳﻲ‬‫ﻛﻪ‬‫ﻳﻜﺪﻳﮕﺮ‬‫را‬‫ﺗﻬﺪﻳﺪ‬‫ﻧﻤﻲ‬‫ﻛﻨﻨﺪ‬ ‫ﻊ‬
)‫ﻣﻴﻨﻴﻤﻢ‬:،‫ﺻﻔﺮ‬‫ﻣﺎﻛﺰﻳﻤﻢ‬:8 * 7/2 = 28(
• 24 / (24+23+20+11) = 31%• 24 / (24+23+20+11) = 31%
• 23 / (24+23+20+11) = 29%
•‫ﻳﻚ‬‫ﻣﺜﺎل‬‫ﺑﺮاي‬‫ﻋﻤﻞ‬Crossover
Off iOff iParentParent11 ParentParent22 OffspringOffspring
11
(3 2 7 5 2 4 1 1) + (2 4 7 4 8 5 5 2) = (3 2 7 4 8 5 5 2)(3, 2, 7, 5, 2, 4, 1, 1) + (2, 4, 7, 4, 8, 5, 5, 2) (3, 2, 7, 4, 8, 5, 5, 2)
‫ژﻧﺘﻴﻚ‬ ‫اﻟﮕﻮرﻳﺘﻢ‬‫ژﻧﺘﻴﻚ‬ ‫اﻟﮕﻮرﻳﺘﻢ‬
function GENETIC-ALGORITHM( population, FITNESS-FN) returns an individual
input: population, a set of individuals
FITNESS-FN, a function that measures the fitness of an individual
repeatrepeat
new_population ← empty set
loop for i from 1 to SIZE(population) do
x ← RANDOM SELECTION(population FITNESS FN)x ← RANDOM_SELECTION(population, FITNESS_FN)
y ← RANDOM_SELECTION(population, FITNESS_FN)
child ← REPRODUCE(x,y)
if (small random probability) then child ← MUTATE(child )if (small random probability) then child ← MUTATE(child )
add child to new_population
population ← new_population
il i di id l i fi h h i h l duntil some individual is fit enough or enough time has elapsed
return the best individual in population, according to FITNESS-FN
‫ﺟﺴﺘﺠﻮ‬ ‫ﻫﺎي‬ ‫اﺳﺘﺮاﺗﮋي‬ ‫ﺑﻨﺪي‬ ‫دﺳﺘﻪ‬‫ﺟﺴﺘﺠﻮ‬ ‫ﻫﺎي‬ ‫اﺳﺘﺮاﺗﮋي‬ ‫ﺑﻨﺪي‬ ‫دﺳﺘﻪ‬
DFS BFS
g = depth
UCS A* A Best
First
General
Search
Greedy
f = h g = depth
h = 0 h ≤ h* f = g + h
First Searchf h

‫اﻛﺘﺸﺎﻓ‬ ‫ﻣﺴﺎﺋﻞ‬‫اﻛﺘﺸﺎﻓﻲ‬ ‫ﻣﺴﺎﺋﻞ‬
•‫ﺗﺎ‬‫ﻛﻨﻮن‬‫ﺗﻤﺎم‬‫اﻟﮕﻮرﻳﺘﻢ‬‫ﻫﺎ‬offline‫ﺑﻮدﻧﺪ‬
Offli‫آ‬ –Offline=‫راه‬‫ﺣﻞ‬‫ﻗﺒﻞ‬‫از‬‫اﺟﺮاي‬‫آن‬‫ﻣﻌﻴﻦ‬‫اﺳﺖ‬
–Online=‫ﻣﺤﺎﺳﺒﻪ‬‫و‬‫ﻋﻤﻞ‬‫ﺑﺼﻮرت‬‫ﻳﻚ‬‫در‬‫ﻣﻴﺎن‬ ‫ﺒ‬‫و‬‫ﻞ‬‫ﻮر‬ ‫ﺑ‬‫ﻳ‬‫ر‬‫ﻴ‬
–‫ﺟﺴﺘﺠﻮي‬online‫ﺑﺮاي‬‫ﻣﺤﻴﻂ‬‫ﻫﺎي‬‫ﭘﻮﻳﺎ‬‫و‬‫ﻧﻴﻤﻪ‬‫ﭘﻮﻳﺎ‬‫ﺿﺮوري‬‫ﻣﻲ‬‫ﺑﺎﺷﺪ‬
‫ﻈ‬‫ﻓ‬ ‫ﮔ‬‫ﺎ‬‫ﻜﺎ‬ ‫ا‬‫ﺎ‬‫ﻠ‬‫ﻏ‬‫ﻜ‬‫ا‬ •‫در‬‫ﻧﻈﺮ‬‫ﮔﺮﻓﺘﻦ‬‫ﺗﻤﺎﻣﻲ‬‫اﻣﻜﺎن‬‫ﻫﺎي‬‫ﻣﺨﺘﻠﻒ‬‫ﻏﻴﺮ‬‫ﻣﻤﻜﻦ‬‫اﺳﺖ‬
•‫اﺳﺘﻔﺎده‬‫ﺷﺪه‬‫در‬‫ﻣﺴﺎﺋﻞ‬‫اﻛﺘﺸﺎﻓﻲ‬ ‫ر‬‫ﻞ‬‫ﻲ‬
–‫ﺣﺎﻟﺖ‬‫ﻫﺎ‬‫و‬‫اﻋﻤﺎل‬‫ﻧﺎﺷﻨﺎﺧﺘﻪ‬
‫ﻚ‬‫ﻚ‬‫ﻚ‬ –‫ﻣﺜﺎل‬:‫ﻳﻚ‬‫روﺑﺎت‬‫در‬‫ﻳﻚ‬‫ﻣﺤﻴﻂ‬،‫ﺟﺪﻳﺪ‬‫ﻳﻚ‬‫ﻧﻮزاد‬‫و‬...
‫ﺟﺴﺘﺠﻮي‬ ‫ﻣﺴﺎﺋﻞ‬online ‫ﺟﺴﺘﺠﻮي‬ ‫ﻣﺴﺎﺋﻞ‬online
•‫داﻧﺶ‬‫ﻋﺎﻣﻞ‬:
–‫ﻋﻤﻞ‬)‫ﻫﺎ‬(:‫ﻟﻴﺴﺖ‬‫اﻋﻤﺎل‬‫ﻣﺠﺎز‬‫در‬‫ﺣﺎﻟﺖ‬s
–c(s, a, s’):‫ﺗﺎﺑﻊ‬‫ﻫﺰﻳﻨﻪ‬‫ﮔﺎم‬)‫ﭘﺲ‬‫از‬‫ﺗﻌﻴﻴﻦ‬s’( ( , , )‫ﺑﻊ‬‫ﺰﻳ‬‫م‬‫ﭘﺲ‬‫ز‬‫ﻴﻴﻦ‬
–GOAL-TEST(s)
•‫ﻋﺎﻣﻞ‬‫ﻣﻲ‬‫ﺗﻮاﻧﺪ‬‫ﺣﺎﻟﺖ‬‫ﻗﺒﻠﻲ‬‫را‬‫ﺗﺸﺨﻴﺺ‬‫دﻫﺪ‬
•‫اﻋﻤﺎل‬‫ﻗﻄﻌ‬‫ﺘﻨﺪ‬ ‫ﻫ‬ ‫اﻋﻤﺎل‬‫ﻗﻄﻌﻲ‬‫ﻫﺴﺘﻨﺪ‬
•‫دﺳﺘﻴﺎﺑﻲ‬‫ﺑﻪ‬‫ﻫﻴﻮرﻳﺴﺘﻴﻚ‬‫ﻗﺎﺑﻞ‬‫ﻗﺒﻮل‬h(s) ‫ﺑﻲ‬ ‫ﻴ‬‫ﺑ‬‫ﻴ‬ ‫ﻴﻮرﻳ‬‫ﺑﻞ‬‫ﺒﻮ‬( )
•‫ﻣﺎﻧﻨﺪ‬‫ﻓﺎﺻﻠﻪ‬‫ﻣﺎﻧﻬﺎﺗﺎﻧﻲ‬
‫ﺟﺴﺘﺠﻮي‬ ‫ﻣﺴﺎﺋﻞ‬online ‫ﺟﺴﺘﺠﻮي‬ ‫ﻣﺴﺎﺋﻞ‬online
•‫ﻫﺪف‬:‫رﺳﻴﺪن‬‫ﺑﻪ‬‫ﺣﺎﻟﺖ‬‫ﻫﺪف‬‫ﺑﺎ‬‫ﺣﺪاﻗﻞ‬‫ﻫﺰﻳﻨﻪ‬
‫ﻛﻞ‬ –‫ﻫﺰﻳﻨﻪ‬=‫ﻛﻞ‬‫ﻫﺰﻳﻨﻪ‬‫ﻣﺴﻴﺮ‬‫ﭘﻴﻤﻮده‬‫ﺷﺪه‬
–‫ﻧﺴﺒﺖ‬‫رﻗﺎﺑﺘﻲ‬=‫ﻣﻘﺎﻳﺴﻪ‬‫ﻫﺰﻳﻨﻪ‬‫ﺑﺎ‬‫ﻫﺰﻳﻨﻪ‬‫ﻣﺴﻴﺮ‬‫راه‬‫ﺣﻞ‬‫در‬‫ﺣﺎﻟﺘﻲ‬‫ﻛﻪ‬‫ﻓﻀﺎي‬ ‫ﺒ‬‫ﻲ‬ ‫ﺑ‬ ‫ر‬‫ﻳ‬‫ﺰﻳ‬‫ﺑ‬‫ﺰﻳ‬‫ﻴﺮ‬‫ر‬‫ﻞ‬‫ر‬‫ﻲ‬‫ي‬
‫ﺟﺴﺘﺠﻮ‬‫ﺷﻨﺎﺧﺘﻪ‬‫ﺷﺪه‬‫ﺑﺎﺷﺪ‬
‫اﻧﺪ‬ ‫ﺗ‬‫ﻧﺎ‬‫د‬ ‫ﺪ‬‫ﺎﺷﺪ‬ –‫ﻣﻲ‬‫ﺗﻮاﻧﺪ‬‫ﻧﺎ‬‫ﻣﺤﺪود‬‫ﺑﺎﺷﺪ‬
–‫در‬‫ﻣﻮاردي‬‫ﻛﻪ‬‫ﻋﺎﻣﻞ‬‫ﺑﻪ‬‫ﻃﻮر‬‫ﺗﺼﺎدﻓﻲ‬
‫ﺑﻪ‬‫ﻳﻚ‬‫ﺑﻦ‬‫ﺑﺴﺖ‬‫ﻣﻲ‬‫رﺳﺪ‬
‫دﺷﻤﻨﺎﻧﻪ‬ ‫اﺳﺘﺪﻻل‬
•‫ﻚ‬‫ﺷ‬‫ا‬‫ﺗ‬‫ﺪ‬ ‫ﻛﻨ‬‫ﻛ‬‫ﻗﺎ‬‫ﺖ‬ ‫ا‬‫ش‬ ‫ﻛﺎ‬‫ﻞ‬ ‫ﺎ‬‫ﻓﻀﺎ‬‫ﺎﻟﺖ‬ •‫ﻳﻚ‬‫دﺷﻤﻦ‬‫را‬‫ﺗﺼﻮر‬‫ﻛﻨﻴﺪ‬‫ﻛﻪ‬‫ﻗﺎدر‬‫اﺳﺖ‬‫در‬‫ﺣﻴﻦ‬‫ﻛﺎوش‬‫ﻋﺎﻣﻞ‬‫ﻓﻀﺎي‬‫ﺣﺎﻟﺖ‬
‫را‬‫ﺑﺴﺎزد‬
•‫ﺣﺎﻟﺖ‬‫ﻫﺎي‬‫ﻣﻼﻗﺎت‬‫ﺷﺪه‬S‫و‬A.‫ﺣﺎﻟﺖ‬‫ﺑﻌﺪي؟‬
–‫در‬‫ﻳﻜﻲ‬‫از‬‫ﻓﻀﺎﻫﺎي‬‫ﺣﺎﻟﺖ‬‫ﺷﻜﺴﺖ‬‫ﻣﻲ‬‫ﺧﻮرد‬ ‫در‬‫ﻳﻜﻲ‬‫از‬‫ﻓﻀﺎﻫﺎي‬‫ﺣﺎﻟﺖ‬‫ﺷﻜﺴﺖ‬‫ﻣﻲ‬‫ﺧﻮرد‬
–‫ﻫﻴﭻ‬‫اﻟﮕﻮرﻳﺘﻤﻲ‬‫ﻧﻤﻲ‬‫ﺗﻮاﻧﺪ‬‫از‬‫ﺑﻦ‬‫ﺑﺴﺖ‬‫ﻫﺎ‬‫در‬‫ﺗﻤﺎﻣﻲ‬‫ﻓﻀﺎﻫﺎي‬‫ﺣﺎﻟﺖ‬‫اﺟﺘﻨﺎب‬‫ﻛﻨﺪ‬

‫ﺟﺴﺘﺠﻮي‬ ‫ﻫﺎي‬ ‫ﻋﺎﻣﻞ‬Online ‫ﺟﺴﺘﺠﻮي‬ ‫ﻫﺎي‬ ‫ﻋﺎﻣﻞ‬Online
•‫ﻋﺎﻣﻞ‬‫ﻳﻚ‬‫ﻧﻘﺸﻪ‬‫از‬‫ﻣﺤﻴﻂ‬‫ﻧﮕﻬﺪاري‬‫ﻣﻲ‬‫ﻛﻨﺪ‬
‫ﻘ‬‫ﺎ‬ ‫ا‬‫اﻛ‬ ‫ا‬‫ﮕﺎ‬ –‫ﻧﻘﺸﻪ‬‫ﺑﺮ‬‫اﺳﺎس‬‫ورودي‬‫ادراﻛﻲ‬‫ﺑﻬﻨﮕﺎم‬‫ﻣﻲ‬‫ﺷﻮد‬
–‫از‬‫اﻳﻦ‬‫ﻧﻘﺸﻪ‬‫ﺑﺮاي‬‫اﻧﺘﺨﺎب‬‫ﻋﻤﻞ‬‫ﺑﻌﺪي‬‫اﺳﺘﻔﺎده‬‫ﻣﻲ‬‫ﺷﻮد‬ ‫ز‬‫ﻳﻦ‬‫ي‬ ‫ﺑﺮ‬‫ب‬‫ﻞ‬‫ي‬ ‫ﺑ‬‫ﻲ‬‫ﻮ‬
•‫ﺑﻪ‬‫ﺗﻔﺎوت‬‫ﻣﺜﻼ‬‫ﺑﺎ‬A*‫دﻗﺖ‬‫ﻛﻨﻴﺪ‬
–‫ﻳﻚ‬‫ﻧﺴﺨﻪ‬online‫ﺗﻨﻬﺎ‬‫ﻣ‬‫ﺗﻮاﻧﺪ‬‫ﻫ‬ ‫ﮔ‬‫را‬‫ش‬ ‫ﮔﺴﺘ‬‫دﻫﺪ‬‫ﻛﻪ‬‫ﺑﻪ‬‫ﻃﻮر‬ ‫ﻳﻚ‬‫ﻧﺴﺨﻪ‬online‫ﺗﻨﻬﺎ‬‫ﻣﻲ‬‫ﺗﻮاﻧﺪ‬‫ﮔﺮﻫﻲ‬‫را‬‫ﮔﺴﺘﺮش‬‫دﻫﺪ‬‫ﻛﻪ‬‫ﺑﻪ‬‫ﻃﻮر‬
‫ﻓﻴﺰﻳﻜﻲ‬‫در‬‫آن‬‫ﻗﺮار‬‫داﺷﺘﻪ‬‫ﺑﺎﺷﺪ‬
Online DFS-Agent
function ONLINE-DFS-AGENT(s’) returns an action
input: s’, a percept identifying current state
t ti lt t bl i d d b ti d t t i iti ll tstatic: result, a table indexed by action and state, initially empty
unexplored, a table that lists for each visited state, the action not yet tried
unbacktracked, a table that lists for each visited state, the backtrack not yet tried
s,a, the previous state and action, initially null
if GOAL TEST(s’) then return stopif GOAL-TEST(s ) then return stop
if s’ is a new state then unexplored[s’] ← ACTIONS(s’)
if s is not null then do
result[a,s] ← s’
add s to the front of unbackedtracked[s’]
if unexplored[s’] is empty thenif unexplored[s ] is empty then
if unbacktracked[s’] is empty then return stop
else a ← an action b such that result[b, s’]=POP(unbacktracked[s’])
else a ← POP(unexplored[s’])
s ← s’
return a
‫ﻋﻤﻘ‬ ‫ﺟﺴﺘﺠﻮي‬online‫ﻣﺜﺎل‬ ‫ﻋﻤﻘﻲ‬ ‫ﺟﺴﺘﺠﻮي‬online‫ﻣﺜﺎل‬ ،
•‫ﻣﺴﺎﻟﻪ‬‫ﻣﺴﻴﺮ‬‫ﭘﺮ‬‫ﭘﻴﭻ‬‫و‬‫ﺧﻢ‬‫ﺑﺮ‬‫روي‬‫ﻳﻚ‬
‫ﻪ‬ ‫ﻔ‬3*3 ‫ﺻﻔﺤﻪ‬3*3
•‫ﺣﺎﻟﺖ‬‫اوﻟﻴﻪ‬:s’=(1, 1) ‫ﺖ‬‫ﻴ‬ ‫و‬:s ( , )
•Result‫و‬Unexplored(UX)‫و‬
Unbacktracked(UB)‫و‬...
‫ﺗﻬ‬‫ﺘﻨﺪ‬ ‫ﻫ‬ ‫ﺗﻬﻲ‬‫ﻫﺴﺘﻨﺪ‬
•S‫و‬a‫ﻧﻴﺰ‬‫ﺗﻬﻲ‬‫ﻫﺴﺘﻨﺪ‬ ‫و‬‫ﻴﺰ‬‫ﻬﻲ‬
• GOAL-TEST((1, 1))?
– S not = G thus false
(1 1) t t ?• (1,1) a new state?
– True
– ACTION((1,1)) -> UX[(1,1)]ACTION((1,1)) UX[(1,1)]
• {RIGHT,UP}
• s is null?
– True (initially)
• UX[(1,1)] empty?
F l– False
• POP(UX[(1,1)]) -> a
a = UP– a = UP
• s = (1,1)
• Return a
• Return a

S’ (1 1)
• GOAL-TEST((1,1))?
S’=(1,1)
(( , ))
• (1,1) a new state?
– false
• s is null?
f l ( (2 1))– false (s = (2, 1))
– result [DOWN, (2, 1)] <- (1,1)
– UB[(1,1)] = {(2,1)}UB[(1,1)] {(2,1)}
• UX[(1,1)] empty?
– False
• a = RIGHT, s = (1,1)
• return a
S’ (1 2)
• GOAL-TEST((1,2))?
S’=(1,2)
(( , ))
• (1,2) a new state?
– True,
UX[(1,2)]={RIGHT,UP,LEFT}
• s is null?s is null?
– false (s = (1,1))
– result [RIGHT, (1,1)] <- (1, 2)
– UB[(1,2)] = {(1,1)}
• UX[(1, 2)] empty?
– False
• a = LEFT, s =(1, 2)
• return a
S’ (1 1)
• GOAL-TEST((1, 1))?
S’=(1,1) – S not = G thus false
• (1, 1) a new state?
– falsefalse
• s is null?
– false (s = (1, 2))
lt [LEFT (1 2)] (1 1)– result [LEFT, (1, 2)] <- (1, 1)
– UB[(1, 1)]={(1, 2), (2, 1)}
• UX[(1, 1)] empty?[( , )] p y
– True
– UB[(1, 1)] empty? False
• a = b for b in result [b (1 1)] = (1 2)• a = b for b in result [b, (1,1)] = (1, 2)
– b = RIGHT
• a = RIGHT, s = (1, 1) …
, ( , )
•‫ﺑﺪﺗﺮﻳﻦ‬‫ﺣﺎﻟﺖ‬:‫ﻫﺮ‬‫ﮔﺮه‬‫دو‬‫ﺑﺎر‬‫ﻣﻼﻗﺎت‬‫ﺷﺪه‬‫اﺳﺖ‬
‫ﻚ‬‫ﻜ‬‫ﻟ‬‫ﻛ‬‫ﻚ‬‫ﻚ‬ •‫ﻳﻚ‬‫ﻋﺎﻣﻞ‬‫ﻣﻤﻜﻦ‬‫اﺳﺖ‬‫در‬‫ﺣﺎﻟﻲ‬‫ﻛﻪ‬‫ﺑﻪ‬‫ﭘﺎﺳﺦ‬‫ﻧﺰدﻳﻚ‬،‫اﺳﺖ‬‫ﻳﻚ‬
‫راه‬‫ﻃﻮﻻﻧﻲ‬‫را‬‫ﺑﭙﻴﻤﺎﻳﺪ‬ ‫ر‬‫ﻲ‬ ‫ﻮ‬‫ر‬‫ﻳ‬ ‫ﺑﭙﻴ‬
•‫ﻳﻚ‬‫روش‬‫ﻋﻤﻴﻖ‬‫ﻛﻨﻨﺪه‬‫ﺗﻜﺮاي‬online‫ﻣﻲ‬‫ﺗﻮاﻧﺪ‬‫اﻳﻦ‬‫ﻣﺸﻜﻞ‬‫را‬
‫ﺣﻞ‬‫ﻛﻨﺪ‬
•‫ي‬ ‫ﺘ‬‫ﻘ‬online‫ﻓﻘﻂ‬‫ﻗﺘ‬‫ﻛﺎ‬‫ﻛﻨﺪ‬‫ﻛﻪ‬‫ﺎل‬ ‫ا‬‫ﻞ‬‫ﻗﺎ‬ •‫ﺟﺴﺘﺠﻮي‬‫ﻋﻤﻘﻲ‬online‫ﻓﻘﻂ‬‫وﻗﺘﻲ‬‫ﻛﺎر‬‫ﻣﻲ‬‫ﻛﻨﺪ‬‫ﻛﻪ‬‫اﻋﻤﺎل‬‫ﻗﺎﺑﻞ‬
‫ﺑﺮﮔﺸﺖ‬‫ﺑﺎﺷﻨﺪ‬

‫ﻣﺤﻠﻲ‬ ‫ﺟﺴﺘﺠﻮي‬online ‫ﻣﺤﻠﻲ‬ ‫ﺟﺴﺘﺠﻮي‬online
•‫ﺗﭙﻪ‬‫ﻧﻮردي‬online‫ﻣﻲ‬‫ﺑﺎﺷﺪ‬
–‫ﻳﻚ‬‫ﺣﺎﻟﺖ‬‫ذﺧﻴﺮه‬‫ﺷﺪه‬‫اﺳﺖ‬
•‫ﻋﻤﻠﻜﺮد‬‫ﺑﺪ‬‫ﺑﻪ‬‫دﻟﻴﻞ‬‫وﺟﻮد‬‫ﻣﺎﻛﺰﻳﻤﻢ‬‫ﻫﺎي‬‫ﻣﺤﻠﻲ‬ ‫ﻋﻤﻠﻜﺮد‬‫ﺑﺪ‬‫ﺑﻪ‬‫دﻟﻴﻞ‬‫وﺟﻮد‬‫ﻣﺎﻛﺰﻳﻤﻢ‬‫ﻫﺎي‬‫ﻣﺤﻠﻲ‬
–‫ﺷﺮوع‬‫ﻣﺠﺪد‬‫ﺗﺼﺎدﻓﻲ‬‫ﻏﻴﺮ‬‫ﻣﻤﻜﻦ‬‫اﺳﺖ‬
‫ﻛ‬‫ﻛ‬)‫ﻻ‬ •‫راه‬‫ﺣﻞ‬:‫ﺣﺮﻛﺖ‬‫ﺗﺼﺎدﻓﻲ‬‫ﺑﺎﻋﺚ‬‫ﻛﺎوش‬‫ﻣﻲ‬‫ﺷﻮد‬)‫ﻣﻲ‬‫ﺗﻮاﻧﺪ‬‫ﺣﺎﻻت‬‫ﺑﺴﻴﺎري‬
‫را‬‫ﺑﻪ‬‫ﺻﻮرت‬‫ﻧﻤﺎﻳﻲ‬‫ﺗﻮﻟﻴﺪ‬‫ﻛﻨﺪ‬( ‫ﻲ‬
‫ﻣﺤﻠ‬ ‫ﺟﺴﺘﺠﻮي‬online ‫ﻣﺤﻠﻲ‬ ‫ﺟﺴﺘﺠﻮي‬online
•‫راه‬‫ﺣﻞ‬2:‫اﺿﺎﻓﻪ‬‫ﻧﻤﻮدن‬‫ﺣﺎﻓﻈﻪ‬‫ﺑﻪ‬‫ﺗﭙﻪ‬‫ﻧﻮرد‬
( ) •‫ذﺧﻴﺮه‬‫ﺑﻬﺘﺮﻳﻦ‬‫ﺗﺨﻤﻴﻦ‬‫ﻓﻌﻠﻲ‬H(s)‫از‬‫ﻫﺰﻳﻨﻪ‬‫رﺳﻴﺪن‬‫ﺑﻪ‬‫ﻫﺪف‬
•H(s)‫در‬‫اﺑﺘﺪا‬‫ﻦ‬ ‫ﺗﺨ‬‫ﻚ‬ ‫ﺘ‬ ‫رﻳ‬ ‫ﻫ‬h(s)‫ﻣ‬‫ﺑﺎﺷﺪ‬ •H(s)‫در‬‫اﺑﺘﺪا‬‫ﺗﺨﻤﻴﻦ‬‫ﻫﻴﻮرﻳﺴﺘﻴﻚ‬h(s)‫ﻣﻲ‬‫ﺑﺎﺷﺪ‬
•‫ﭘﺲ‬‫از‬‫آن‬‫ﺑﺮ‬‫اﺳﺎس‬‫ﺗﺠﺮﺑﻪ‬‫ﺑﻬﻨﮕﺎم‬‫ﻣﻲ‬‫ﺷﻮد‬)‫ﺷﻜﻞ‬‫ﭘﺎﻳﻴﻦ‬( ‫م‬‫ﻞ‬
Learning real-time A*
function LRTA*-COST (s, a, s’,H) return an cost estimate
if s’ is undefined the return h(s)if s is undefined the return h(s)
else return c(s, a, s’) + H[s’]
function LRTA* AGENT (s’) return an actionfunction LRTA*-AGENT (s ) return an action
input: s’, a percept identifying current state
static: result, a table indexed by action and state, initially empty
H t bl f t ti t i d d b t t i iti ll tH, a table of cost estimates indexed by state, initially empty
s,a, the previous state and action, initially null
if GOA T S ( ’)if GOAL-TEST (s’) then return stop
if s’ is a new state (not in H) then H[s’] ← h(s’)
unless s is null
result[a,s] ← s’
H[s] ← MIN LRTA*-COST (s, b, result [b, s], H)
b ∈ ACTIONS (s)
a ← an action b in ACTIONS (s’) that minimizes LRTA*-COST (s’, b, result [b, s’], H)
s ← s’
return a
etu a

AI ch4

More Related Content

Recently uploaded (20)

Featured (20)

AI ch4