4. ﻣﺤﻴﻂﻣﺤﻴﻂﻣﺤﻴﻂﻣﺤﻴﻂ
•ﻫﺮﻣﺤﻴﻂدارايﻣﺠﻤﻮﻋﻪايازﺣﺎﻟﺖﻫﺎﻣﻲﺑﺎﺷﺪ:
ﻂﻈ ﻟﻓﻘﻂﻜااﺎﻟﺎﺎ –ﻣﺤﻴﻂدرﻫﺮﻟﺤﻈﻪﻓﻘﻂدرﻳﻜﻲازاﻳﻦﺣﺎﻟﺖﻫﺎﻣﻲﺑﺎﺷﺪ.
•ﻣﺜﺎل:دﻧﻴﺎيﻣﻜﺶ
S = {1, 2, 3, 4, 5, 6, 7, 8}
N. Razavi- AI course- 2005 13
ﻣﺤﻴﻂ و ﻋﺎﻣﻞﻣﺤﻴﻂ و ﻋﺎﻣﻞﻣﺤﻴﻂ و ﻋﺎﻣﻞﻣﺤﻴﻂ و ﻋﺎﻣﻞ
•درﻟﺤﻈﻪ،ﺷﺮوعﻣﺤﻴﻂدرﻳﻜﻲازﺣﺎﻟﺖﻫﺎيﻣﻤﻜﻦﻣﻲﺑﺎﺷﺪ
ﻞﻞ ﺎﻂﺚ ﺎﺎﻟﻂ –ﻋﻤﻞﻋﺎﻣﻞدر،ﻣﺤﻴﻂﺑﺎﻋﺚﺗﻐﻴﻴﺮﺣﺎﻟﺖﻣﺤﻴﻂﻣﻲﺷﻮد
•ﺣﺎﻟﺖﻓﻌﻠﻲ:Si ﻲ
•ﻋﻤﻞﻋﺎﻣﻞ:Action
•ﺣﺎﻟﺖﺑﻌﺪي:Sj
Si Sj
Action
ﺣﺎﻟﺖﺑﻌﺪي:Sj
•ﺜﺎل:ﺎي دﻧﻜﺶ •ﻣﺜﺎل:دﻧﻴﺎيﻣﻜﺶ
S CSUCK
N. Razavi- AI course- 2005 14
ﻣﺤﻴﻂ اﻧﻮاعﻣﺤﻴﻂ اﻧﻮاعﻣﺤﻴﻂ اﻧﻮاعﻣﺤﻴﻂ اﻧﻮاع
•ﻛﺎﻣﻼﻗﺎﺑﻞﻣﺸﺎﻫﺪه)درﻣﻘﺎﺑﻞﻣﺸﺎﻫﺪهﭘﺬﻳﺮﺟﺰﺋﻲ(:ﻣﺤﻴﻄﻲﻛﻪدرآندر
ﻫﺮﻟﺤﻈﻪاززﻣﺎنﺣﺴﮕﺮﻫﺎيﻋﺎﻣﻞﺑﻪآناﻣﻜﺎندﺳﺘﻴﺎﺑﺑﻪﺣﺎﻟﺖﻛﺎﻣﻞ ﻫﺮﻟﺤﻈﻪاززﻣﺎنﺣﺴﮕﺮﻫﺎيﻋﺎﻣﻞﺑﻪآناﻣﻜﺎندﺳﺘﻴﺎﺑﻲﺑﻪﺣﺎﻟﺖﻛﺎﻣﻞ
ﻣﺤﻴﻂراﻣﻲدﻫﻨﺪ.
•ﻣﺜﺎل:دﻧﻴﺎيﻣﻜﺶ–ﺣﺴﮕﺮﻫﺎ:[location, status] ل:ي ﻴﺶﺮ:
–ﺗﺸﺨﻴﺺﻣﻜﺎن:ﭼﭗﻳﺎراﺳﺖ
ﺗﺸﺨﺖ ﺿ:ﺰ ﺗﺎﻒ ﻛﺜ
[ , ]
–ﺗﺸﺨﻴﺺوﺿﻌﻴﺖ:ﺗﻤﻴﺰﻳﺎﻛﺜﻴﻒ
[ LEFT, [CLEAN, DIRTY] ]
N. Razavi- AI course- 2005 15
ﻣﺤﻴﻂ اﻧﻮاعﻣﺤﻴﻂ اﻧﻮاعﻣﺤﻴﻂ اﻧﻮاعﻣﺤﻴﻂ اﻧﻮاع
•ﻗﻄﻌ:)درﻣﻘﺎﺑﻞاﺗﻔﺎﻗ(:ﺣﺎﻟﺖﺑﻌﺪيﻂ ﻣﺤﻛﺎﻣﻼﻠﻪ ﺑﻮﺳﺣﺎﻟﺖﻓﻌﻠو ﻗﻄﻌﻲ:)درﻣﻘﺎﺑﻞاﺗﻔﺎﻗﻲ(:ﺣﺎﻟﺖﺑﻌﺪيﻣﺤﻴﻂﻛﺎﻣﻼﺑﻮﺳﻴﻠﻪﺣﺎﻟﺖﻓﻌﻠﻲو
ﻋﻤﻞاﻧﺠﺎمﺷﺪهﺗﻮﺳﻂﻋﺎﻣﻞﻗﺎﺑﻞﺗﻌﻴﻴﻦﻣﻲﺑﺎﺷﺪ.
ﮔﮕﮕآ –اﮔﺮﻣﺤﻴﻂﺑﻪﺟﺰدرﻣﻮردﻋﻤﻞﻋﺎﻣﻞﻫﺎيدﻳﮕﺮﻗﻄﻌﻲ،ﺑﺎﺷﺪآﻧﮕﺎهﻣﺤﻴﻂ
اﺳﺘﺮاﺗﮋﻳﻚﻣﻲﺑﺎﺷﺪ.
SUCK
S
S
SUCK
ﻗﻄﻌﻲ
SUCK
اﺗﻔﺎﻗﻲ
N. Razavi- AI course- 2005 16
5. ﻣﺤﻴﻂ اﻧﻮاعﻣﺤﻴﻂ اﻧﻮاعﻣﺤﻴﻂ اﻧﻮاعﻣﺤﻴﻂ اﻧﻮاع
•اﭘﻴﺰودﻳﻚ)درﻣﻘﺎﺑﻞﺗﺮﺗﻴﺒﻲ(:ﺗﺠﺮﺑﻪﻋﺎﻣﻞﺑﻪ»دورهﻫﺎي«ﻏﻴﺮﻗﺎﺑﻞﺗﺠﺰﻳﻪ
ﺗﻘﺴﻴﻢﻣﺷﻮد)ﻫﺮدورهﺷﺎﻣﻞادراكﻋﺎﻣﻞوﺳﭙﺲاﻧﺠﺎمﻳﻚﻋﻤﻞ ﺗﻘﺴﻴﻢﻣﻲﺷﻮد)ﻫﺮدورهﺷﺎﻣﻞادراكﻋﺎﻣﻞوﺳﭙﺲاﻧﺠﺎمﻳﻚﻋﻤﻞ
ﻣﻲﺑﺎﺷﺪ(واﻧﺘﺨﺎبﻋﻤﻞدرﻫﺮدورهﺗﻨﻬﺎﺑﻪﺧﻮدﻫﻤﺎندورهﺑﺴﺘﮕﻲدارد.
•ﻣﺜﺎل:روﺑﺎتﻛﻨﺘﺮلﻛﻨﻨﺪهﻛﻴﻔﻴﺖ لروﺑﺮلﻴ ﻴ
Episode 1 Episode 2 Episode 3
123
p
234
p
345
p
Accept Reject Accept
N. Razavi- AI course- 2005 17
ﻣﺤﻴﻂ اﻧﻮاعﻣﺤﻴﻂ اﻧﻮاعﻣﺤﻴﻂ اﻧﻮاعﻣﺤﻴﻂ اﻧﻮاع
•اﻳﺴﺘﺎ)درﻣﻘﺎﺑﻞﭘﻮﻳﺎ(:ﻣﺤﻴﻂدرﺣﻴﻦﺳﻨﺠﺶﻋﺎﻣﻞ)ﺑﺮاياﻧﺘﺨﺎبﻋﻤﻞ(ﺗﻐﻴﻴﺮ
ﻧﻤﻛﻨﺪاﮔﺮﺧﻮدﻣﺤﻴﻂﺑﺎﮔﺬﺷﺖزﻣﺎنﺗﻐﻴﻴﺮﻧﻜﻨﺪوﻟﻣﻌﻴﺎرﻛﺎرآﻳ ﻧﻤﻲﻛﻨﺪ.اﮔﺮﺧﻮدﻣﺤﻴﻂﺑﺎﮔﺬﺷﺖزﻣﺎنﺗﻐﻴﻴﺮﻧﻜﻨﺪوﻟﻲﻣﻌﻴﺎرﻛﺎرآﻳﻲ
ﻋﺎﻣﻞﺗﻐﻴﻴﺮ،ﻛﻨﺪآﻧﮕﺎهﻣﺤﻴﻂﻧﻴﻤﻪﭘﻮﻳﺎﻣﻲﺑﺎﺷﺪ.
t t’
ﻣﺤﻴﻂ ﺳﻨﺠﺶ
S S اﻳﺴﺘﺎ
S S’ ﭘﻮﻳﺎ
N. Razavi- AI course- 2005 18
ﻣﺤﻴﻂ اﻧﻮاعﻣﺤﻴﻂ اﻧﻮاعﻣﺤﻴﻂ اﻧﻮاعﻣﺤﻴﻂ اﻧﻮاع
•ﮔﺴﺴﺘﻪ)درﻣﻘﺎﺑﻞﭘﻴﻮﺳﺘﻪ(:ﻣﺤﻴﻄﻲﻛﻪدرآنﺗﻌﺪادﻣﺤﺪودو
ﻣﺘﻤﺎﻳﺰيازدركﻫﺎوﻋﻤﻞﻫﺎيﻛﺎﻣﻼواﺿﺢﻳﻒ ﺗﻌﺷﺪهﺑﺎﺷﺪ ﻣﺘﻤﺎﻳﺰيازدركﻫﺎوﻋﻤﻞﻫﺎيﻛﺎﻣﻼواﺿﺢﺗﻌﺮﻳﻒﺷﺪهﺑﺎﺷﺪ.
•درﻣﺤﻴﻂ،ﮔﺴﺴﺘﻪﻣﺠﻤﻮﻋﻪﺣﺎﻻتﻣﺤﻴﻂﻳﻚﻣﺠﻤﻮﻋﻪﮔﺴﺴﺘﻪ
ﻣﺑﺎﺷﺪوﺣﺎﻻتﺑﺴﺎدﮔﻗﺎﺑﻞﺗﻤﺎﻳﺰﻣﺑﺎﺷﻨﺪ ﻣﻲﺑﺎﺷﺪوﺣﺎﻻتﺑﺴﺎدﮔﻲﻗﺎﺑﻞﺗﻤﺎﻳﺰﻣﻲﺑﺎﺷﻨﺪ.
–ﻣﺜﺎل:ﻣﺤﻴﻂدﻧﻴﺎيﻣﻜﺶ
– State = {1, 2, …, 8}
– Action = {Left, Right, Suck, NoOp}
– Percept = {[Left, Clean], [Left, Dirty], [Right, Clean], …}
N. Razavi- AI course- 2005 19
ﻣﺤﻴﻂ اﻧﻮاعﻣﺤﻴﻂ اﻧﻮاعﻣﺤﻴﻂ اﻧﻮاعﻣﺤﻴﻂ اﻧﻮاع
•ﺗﻚﻋﺎﻣﻠﻲ)درﺑﺮاﺑﺮﭼﻨﺪﻋﺎﻣﻠﻲ(:ﻳﻚﻋﺎﻣﻞﺧﻮدشﺑﻪﺗﻨﻬﺎﻳﻲدر
ﻂﻞﻛﻨﺪ ﻣﺤﻴﻂﻋﻤﻞﻣﻲﻛﻨﺪ.
–ﻣﺜﺎل:ﻣﺤﻴﻂﻋﺎﻣﻞﺣﻞﻛﻨﻨﺪهﺟﺪولﻛﻠﻤﺎتﻣﺘﻘﺎﻃﻊودﻧﻴﺎيﻣﻜﺶ ﻞﻞﻊﺶ
ﻠﻞﻛﮕ ﻜﻞ •ﭼﻨﺪﻋﺎﻣﻠﻲ:ﺗﻌﺪاديﻋﺎﻣﻞﻛﻪﺑﺎﻳﻜﺪﻳﮕﺮدرﺗﻌﺎﻣﻞﻣﻲﺑﺎﺷﻨﺪ.
–ﻣﺜﺎل:ﺷﻄﺮﻧﺞ)رﻗﺎﺑﺘﻲ(،روﺑﻮﻛﺎپ)ﺑﻴﻦاﻋﻀﺎيﻳﻚﺗﻴﻢﻫﻤﻴﺎريوﺑﻴﻦ ل:ﺞﺮ)ﻲ ﺑ ر(،پ روﺑﻮ)ﺑﻴﻦي ﻀ اﻳﻚﻴﻢري ﻤﻴوﺑﻴﻦ
اﻋﻀﺎيدوﺗﻴﻢرﻗﺎﺑﺘﻲ(،ﻣﺤﻴﻂﺗﺎﻛﺴﻲﺧﻮدﻛﺎر)ﻫﻤﻴﻴﺎريﺟﺰﻳﻲ(
N. Razavi- AI course- 2005 20
6. ﻣﺤﻴﻂ اﻧﻮاعﻣﺤﻴﻂ اﻧﻮاعﻴ ع ﻮﻴ ع ﻮ
ﺗﺎﻛﺴﻲ راﻧﻨﺪﮔﻲ ﺑﺪون ﺷﻄﺮﻧﺞ
ﺳﺎﻋﺖ
ﺳﺎﻋﺖ ﺑﺎ ﺷﻄﺮﻧﺞ
ﺳﺎﻋﺖ
ﺧﻴﺮ ﺑﻠﻪ ﺑﻠﻪ ﻣﺸﺎﻫﺪه ﻗﺎﺑﻞ ﻛﺎﻣﻼ
ﻚ ﻚﺧﻴﺮ اﺳﺘﺮاﺗﮋﻳﻚ اﺳﺘﺮاﺗﮋﻳﻚ ﻗﻄﻌﻲ
ﺧﻴﺮ ﺧﻴﺮ ﺧﻴﺮ اي دوره
ﺧﻴﺮ ﺑﻠﻪ ﭘﻮﻳﺎ ﻧﻴﻤﻪ اﻳﺴﺘﺎ
ﻠ ﻠ ﮔﺧﻴﺮ ﺑﻠﻪ ﺑﻠﻪ ﮔﺴﺴﺘﻪ
ﺧﻴﺮ ﺧﻴﺮ ﺧﻴﺮ ﻋﺎﻣﻠﻲ ﺗﻚ
•ﺑﺎﺷﺪ ﻣﻲ ﻋﺎﻣﻞ ﻃﺮاﺣﻲ ﻛﻨﻨﺪه ﺗﻌﻴﻴﻦ زﻳﺎدي ﻣﻴﺰان ﺑﻪ ﻣﺤﻴﻂ ﻧﻮع. ع
•واﻗﻌﻲ دﻧﻴﺎي:ﭼﻨﺪﻋﺎﻣﻠﻲ و ﭘﻴﻮﺳﺘﻪ ،ﭘﻮﻳﺎ ،ﺗﺮﺗﻴﺒﻲ ،اﺗﻔﺎﻗﻲ ،ﺟﺰﺋﻲ ﭘﺬﻳﺮ ﻣﺸﺎﻫﺪه
N. Razavi- AI course- 2005 21
ﻋﺎﻣﻞ ﻫﺎي ﺑﺮﻧﺎﻣﻪ و ﺗﻮاﺑﻊﻋﺎﻣﻞ ﻫﺎي ﺑﺮﻧﺎﻣﻪ و ﺗﻮاﺑﻊﻋﺎﻣﻞ ﻫﺎي ﺑﺮﻧﺎﻣﻪ و ﺗﻮاﺑﻊﻋﺎﻣﻞ ﻫﺎي ﺑﺮﻧﺎﻣﻪ و ﺗﻮاﺑﻊ
•ﻳﻚﻋﺎﻣﻞﻛﺎﻣﻼﺑﻮﺳﻴﻠﻪﺗﺎﺑﻊﻋﺎﻣﻞﻣﺸﺨﺺﻣﻲﺷﻮد.
آﻛﮕﻛ –ﻳﺎدآوري:ﺗﺎﺑﻊﻋﺎﻣﻞدﻧﺒﺎﻟﻪادراﻛﻲراﺑﻪﻋﻤﻞﻧﮕﺎﺷﺖﻣﻲﻛﻨﺪ.
•ﻳﻚﺗﺎﺑﻊﻋﺎﻣﻞ)ﻳﺎﻳﻚﻛﻼسﻫﻢارزيﻛﻮﭼﻚ(ﻣﻨﻄﻘﻲ
( i l)(rational)ﻣﻲﺑﺎﺷﺪ.
•ﻫﺪف:ﻳﺎﻓﺘﻦروﺷﻲﺑﻪﻣﻨﻈﻮرﭘﻴﺎدهﺳﺎزيﺗﺎﺑﻊﻋﺎﻣﻞﻣﻨﻄﻘﻲﺑﻪﻃﻮر ﻊ
ﻣﺨﺘﺼﺮوﻣﻔﻴﺪ
N. Razavi- AI course- 2005 22
ﺟﺴﺘﺠﻮ ﺟﺪول ﺑﺮ ﻣﺒﺘﻨ ﻋﺎﻣﻞﺟﺴﺘﺠﻮ ﺟﺪول ﺑﺮ ﻣﺒﺘﻨ ﻋﺎﻣﻞﺟﺴﺘﺠﻮ ﺟﺪول ﺑﺮ ﻣﺒﺘﻨﻲ ﻋﺎﻣﻞﺟﺴﺘﺠﻮ ﺟﺪول ﺑﺮ ﻣﺒﺘﻨﻲ ﻋﺎﻣﻞ
•ﻳﻚروشﺑﻪﻣﻨﻈﻮرﺗﻮﺻﻴﻒﺗﺎﺑﻊﻋﺎﻣﻞ
•ﻧﺸﺎنﻨﺪﺖ ﺎﻟ ﻓﻨﺎاﺎﻟ ﻧاﻛ اﻜ •ﻧﺸﺎندﻫﻨﺪهﻓﻌﺎﻟﻴﺖﻣﻨﺎﺳﺐﺑﺮايﻫﺮدﻧﺒﺎﻟﻪادراﻛﻲﻣﻤﻜﻦ
•ﻣﺜﺎل:ﺟﺪولدﻧﻴﺎيﺟﺎروﺑﺮﻗﻲPercept Sequence Actionp q
[A, Clean] Right
[A Dirty] Suck[A, Dirty] Suck
[B, Clean] Left
[B Dirty] Suck[B, Dirty] Suck
[A, Clean], [A, Clean] Right
[A Cl ] [A Di t ] S k[A, Clean], [A, Dirty] Suck
…
N. Razavi- AI course- 2005 23
ﺟﺴﺘﺠﻮ ﺟﺪول ﺑﺮ ﻣﺒﺘﻨ ﻋﺎﻣﻞ ﺑﺮﻧﺎﻣﻪﺟﺴﺘﺠﻮ ﺟﺪول ﺑﺮ ﻣﺒﺘﻨ ﻋﺎﻣﻞ ﺑﺮﻧﺎﻣﻪﺟﺴﺘﺠﻮ ﺟﺪول ﺑﺮ ﻣﺒﺘﻨﻲ ﻋﺎﻣﻞ ﺑﺮﻧﺎﻣﻪﺟﺴﺘﺠﻮ ﺟﺪول ﺑﺮ ﻣﺒﺘﻨﻲ ﻋﺎﻣﻞ ﺑﺮﻧﺎﻣﻪ
function TABLE-DRIVEN-AGENT( percept) returns an action
static: percepts, a sequence, initially empty
table, a table of actions, indexed by percept sequence,, , y p p q ,
initially fully specified
append percept to the end of percepts
i LOO ( bl )action LOOKUP( percepts, table)
return action
N. Razavi- AI course- 2005 24
7. ﺟﺴﺘﺠﻮ ﺟﺪول ﺑﺮ ﻣﺒﺘﻨ ﻋﺎﻣﻞﺟﺴﺘﺠﻮ ﺟﺪول ﺑﺮ ﻣﺒﺘﻨ ﻋﺎﻣﻞﺟﺴﺘﺠﻮ ﺟﺪول ﺑﺮ ﻣﺒﺘﻨﻲ ﻋﺎﻣﻞﺟﺴﺘﺠﻮ ﺟﺪول ﺑﺮ ﻣﺒﺘﻨﻲ ﻋﺎﻣﻞ
•ﻣﻌﺎﻳﺐ:
15 –ﺟﺪولﺑﺴﻴﺎرﻋﻈﻴﻢ)ﻣﺜﻼدرﺷﻄﺮﻧﺞ10150ﺳﻄﺮ(
–زﻣﺎنﺑﺴﻴﺎرزﻳﺎدﺑﺮاياﻳﺠﺎدﺟﺪولواﺣﺘﻤﺎلﺑﺎﻻيﺧﻄﺎ زر ﻴ ﺑزﻳي ﺑﺮﻳووي ﺑ
–ﻋﺪمﺧﻮدﻣﺨﺘﺎري
ﻠﮔﮔ –ﺣﺘﻲﺑﺎﻗﺎﺑﻠﻴﺖ،ﻳﺎدﮔﻴﺮيﻧﻴﺎزﺑﻪزﻣﺎنﺑﺴﻴﺎرزﻳﺎديﺑﺮايﻳﺎدﮔﻴﺮي
ﻣﺪاﺧﻞﺟﺪولدارد. ﻞ
N. Razavi- AI course- 2005 25
ﻫﺎ ﻋﺎﻣﻞ اﻧﻮاعﻫﺎ ﻋﺎﻣﻞ اﻧﻮاعﻫﺎ ﻋﺎﻣﻞ اﻧﻮاعﻫﺎ ﻋﺎﻣﻞ اﻧﻮاع
(G li ) •ﭼﻬﺎرﻧﻮعاﺻﻠﻲﺑﻪﺗﺮﺗﻴﺐاﻓﺰاﻳﺶﻋﻤﻮﻣﻴﺖ(Generality):
–ﻋﺎﻣﻞﻫﺎيواﻛﻨﺸﺳﺎده(Simple reflex) ﻋﺎﻣﻞﻫﺎيواﻛﻨﺸﻲﺳﺎده(Simple reflex)
–ﻋﺎﻣﻞﻫﺎيواﻛﻨﺸﻲﻣﺒﺘﻨﻲﺑﺮﻣﺪل(Model-based reflex)
–ﻋﺎﻣﻞﻫﺎيﻣﺒﺘﻨﻲﺑﺮﻫﺪف(Goal-based)
–ﻋﺎﻣﻞﻫﺎيﺘﻨ ﻣﺑﺳﻮدﻣﻨﺪي(Utility-based) ﻋﺎﻣﻞﻫﺎيﻣﺒﺘﻨﻲﺑﺮﺳﻮدﻣﻨﺪي(Utility based)
N. Razavi- AI course- 2005 26
ﺳﺎده واﻛﻨﺸﻲ ﻫﺎي ﻋﺎﻣﻞﺳﺎده واﻛﻨﺸﻲ ﻫﺎي ﻋﺎﻣﻞﺳﺎده واﻛﻨﺸﻲ ﻫﺎي ﻋﺎﻣﻞﺳﺎده واﻛﻨﺸﻲ ﻫﺎي ﻋﺎﻣﻞ
•ﺳﺎدهﺗﺮﻳﻦﻧﻮعﻋﺎﻣﻞ ﺮﻳﻦﻮعﻞ
•درﻫﺮ،ﻟﺤﻈﻪﻋﻤﻞﺗﻨﻬﺎﺑﺮاﺳﺎسدركﻓﻌﻠﻲاﻧﺘﺨﺎبﻣﻲﺷﻮد
•ﻣﺜﺎل:
function REFLEX-VACCUM-AGENT( [location, status]) returns an action
if Di h S kif status = Dirty then return Suck
else if location = A then return Right
else if location = B then return Left
•ﺷﺎﻣﻞﻗﻮاﻧﻴﻦﺷﺮط-ﻋﻤﻞﻣﺎﻧﻨﺪ:
else if location B then return Left
ﻞﻴﻦ ﻮﺮﻞ
–“اﮔﺮﭼﺮاغﺗﺮﻣﺰاﺗﻮﻣﻮﺑﻴﻞﺟﻠﻮﻳﻲروﺷﻦ،ﺷﺪآﻧﮕﺎهﺗﺮﻣﺰﻛﻦ”
N. Razavi- AI course- 2005 27
ﺳﺎده واﻛﻨﺸﻲ ﻫﺎي ﻋﺎﻣﻞ ﺳﺎﺧﺘﺎرﺳﺎده واﻛﻨﺸﻲ ﻫﺎي ﻋﺎﻣﻞ ﺳﺎﺧﺘﺎرﻲ و رﻲ و ر
Agent Sensors
E
What the world
is like now
nvironnmen
Wh t ti I
t
What action I
should do now
Condition-action rules
Actuators
N. Razavi- AI course- 2005 28
8. ﺳﺎده واﻛﻨﺸ ﻋﺎﻣﻞ ﺑﺮﻧﺎﻣﻪﺳﺎده واﻛﻨﺸ ﻋﺎﻣﻞ ﺑﺮﻧﺎﻣﻪﺳﺎده واﻛﻨﺸﻲ ﻋﺎﻣﻞ ﺑﺮﻧﺎﻣﻪﺳﺎده واﻛﻨﺸﻲ ﻋﺎﻣﻞ ﺑﺮﻧﺎﻣﻪ
function SIMPLE-REFLEX-AGENT( percept) returns an action
t ti l t f diti ti lstatic: rules, a set of condition-action rules
state INTERPRET-INPUT( percept)
rule RULE MATCH( state rules)rule RULE-MATCH( state, rules)
action RULE-ACTION[ rule]
return action
N. Razavi- AI course- 2005 29
ﻣﺪل ﺑﺮ ﻣﺒﺘﻨ واﻛﻨﺸ ﻫﺎي ﻋﺎﻣﻞ)دار ﺎﻓﻈﻪ( ﻣﺪل ﺑﺮ ﻣﺒﺘﻨ واﻛﻨﺸ ﻫﺎي ﻋﺎﻣﻞ)دار ﺎﻓﻈﻪ( ﻣﺪل ﺑﺮ ﻣﺒﺘﻨﻲ واﻛﻨﺸﻲ ﻫﺎي ﻋﺎﻣﻞ)دار ﺣﺎﻓﻈﻪ( ﻣﺪل ﺑﺮ ﻣﺒﺘﻨﻲ واﻛﻨﺸﻲ ﻫﺎي ﻋﺎﻣﻞ)دار ﺣﺎﻓﻈﻪ(
•ﻋﺎﻣﻞواﻛﻨﺸﻲﺳﺎدهدرﺻﻮرﺗﻲﻛﺎرﻣﻲﻛﻨﺪﻛﻪﻣﺤﻴﻂﻛﺎﻣﻼﻗﺎﺑﻞ
ﺪ ﺸﺎﺎﺷﺪ ﻣﺸﺎﻫﺪهﺑﺎﺷﺪ
•اﮔﺮﻣﺤﻴﻂﻣﺸﺎﻫﺪهﭘﺬﻳﺮﺟﺰﺋﻲ،ﺑﺎﺷﺪﭘﻴﮕﻴﺮيﺗﻐﻴﻴﺮاتدﻧﻴﺎﻻزم ﺮﻴﻳﺮ ﭘﻲ ﺟﺰﺑﻴﺮي ﭘﻴﻴﻴﺮﻴزم
اﺳﺖ
ﻚ •ﻣﺜﺎل:ﺗﺎﻛﺴﻲاﺗﻮﻣﺎﺗﻴﻚ
•ﺘﻠﺰم ﻣدوع ﻧداﻧﺶ •ﻣﺴﺘﻠﺰمدوﻧﻮعداﻧﺶ
–ﻧﺤﻮهﺗﻐﻴﻴﺮدﻧﻴﺎ
–ﺗﺎﺛﻴﺮاﻋﻤﺎلﻋﺎﻣﻞﺑﺮدﻧﻴﺎ
N. Razavi- AI course- 2005 30
ﻣﺪل ﺑﺮ ﻣﺒﺘﻨﻲ واﻛﻨﺸﻲ ﻫﺎي ﻋﺎﻣﻞﻣﺪل ﺑﺮ ﻣﺒﺘﻨﻲ واﻛﻨﺸﻲ ﻫﺎي ﻋﺎﻣﻞل ﺑﺮ ﻲ ﺒ ﻲ و ي ﻞل ﺑﺮ ﻲ ﺒ ﻲ و ي ﻞ
Sensors
State
E
What the world
is like now
How the world evolves
nviron
What my actions do
nmen
Wh t ti I
t
What action I
should do now
Condition-action rules
Agent Actuators
N. Razavi- AI course- 2005 31
ﻣﺪل ﺑﺮ ﻣﺒﺘﻨ واﻛﻨﺸ ﻫﺎي ﻋﺎﻣﻞ ﺑﺮﻧﺎﻣﻪﻣﺪل ﺑﺮ ﻣﺒﺘﻨ واﻛﻨﺸ ﻫﺎي ﻋﺎﻣﻞ ﺑﺮﻧﺎﻣﻪﻣﺪل ﺑﺮ ﻣﺒﺘﻨﻲ واﻛﻨﺸﻲ ﻫﺎي ﻋﺎﻣﻞ ﺑﺮﻧﺎﻣﻪﻣﺪل ﺑﺮ ﻣﺒﺘﻨﻲ واﻛﻨﺸﻲ ﻫﺎي ﻋﺎﻣﻞ ﺑﺮﻧﺎﻣﻪ
function REFLEX-AGENT-WITH-STATE( percept) returns an action( p p )
static: state, a description of the current world state
rules, a set of condition-action rules
action, the most recent action, initially none
state UPDATE-STATE( state, action, percept)
rule RULE-MATCH( state, rules)rule RULE MATCH( state, rules)
action RULE-ACTION[ rule]
return actionreturn action
N. Razavi- AI course- 2005 32
9. ﻫﺪف ﺑﺮ ﻣﺒﺘﻨ ﻫﺎي ﻋﺎﻣﻞﻫﺪف ﺑﺮ ﻣﺒﺘﻨ ﻫﺎي ﻋﺎﻣﻞﻫﺪف ﺑﺮ ﻣﺒﺘﻨﻲ ﻫﺎي ﻋﺎﻣﻞﻫﺪف ﺑﺮ ﻣﺒﺘﻨﻲ ﻫﺎي ﻋﺎﻣﻞ
•اﻃﻼﻋﺎتﻻزمﺑﺮايﺗﺼﻤﻴﻢﮔﻴﺮيدرﻣﻮردﻋﻤﻠﻲﻛﻪﺑﺎﻳﺪاﻧﺠﺎم
ﺷﻮد:ﺷﻮد:
–اﻃﻼﻋﺎتﻣﺮﺑﻮطﺑﻪﺣﺎﻟﺖﻓﻌﻠﻲ
–اﻃﻼﻋﺎتﻫﺪف)ﺗﻮﺻﻴﻒﻣﻮﻗﻌﻴﺖﻣﻄﻠﻮب(
–ﻣﺜﺎل:ﻋﻤﻞﻣﻨﺎﺳﺐﺑﺮايﺗﺎﻛﺴﻲاﺗﻮﻣﺎﺗﻴﻚدرﻳﻚﭼﻬﺎرراهﻛﺪام ﻞﺐي ﺑﺮﻲﻴ ﻮرﻳر ﭼﻬرم
اﺳﺖ؟)،ﺑﺎﻻﭘﺎﻳﻴﻦ،ﭼﭗراﺳﺖ(
•اﮔاي ﺑﺪنﺑﻪﻫﺪفﺎز ﻧﺑﻪﭼﻨﺪﻳﻦﻞ ﻋﺑﺎﺷﺪ •اﮔﺮﺑﺮايرﺳﻴﺪنﺑﻪﻫﺪفﻧﻴﺎزﺑﻪﭼﻨﺪﻳﻦﻋﻤﻞﺑﺎﺷﺪ
–ﺟﺴﺘﺠﻮ(search)
–ﺑﺮﻧﺎﻣﻪرﻳﺰي(planning)
N. Razavi- AI course- 2005 33
ﻫﺪف ﺑﺮ ﻣﺒﺘﻨﻲ ﻫﺎي ﻋﺎﻣﻞﻫﺪف ﺑﺮ ﻣﺒﺘﻨﻲ ﻫﺎي ﻋﺎﻣﻞﻲﻲ
Sensors
State
E
What the world
is like now
How the world evolves
nviron
What my actions do
What it will be like
nmen
Wh t ti I
if I do action A
t
What action I
should do now
Goals
Agent Actuators
N. Razavi- AI course- 2005 34
ﻣﺜﺎلﮔﺮا ﻫﺪف ﻋﺎﻣﻞ ﻣﺜﺎلﮔﺮا ﻫﺪف ﻋﺎﻣﻞ ﻣﺜﺎل:ﮔﺮا ﻫﺪف ﻋﺎﻣﻞ ﻣﺜﺎل:ﮔﺮا ﻫﺪف ﻋﺎﻣﻞ
A
N. Razavi- AI course- 2005 35
ﻣﺜﺎل:ﮔﺮا ﻫﺪف ﻋﺎﻣﻞ ﻣﺜﺎل:ﮔﺮا ﻫﺪف ﻋﺎﻣﻞ
[UP, UP, UP, RIGHT]
[RIGHT RIGHT RIGHT UP UP UP LEFT LEFT][RIGHT, RIGHT, RIGHT, UP, UP, UP, LEFT, LEFT]
A
N. Razavi- AI course- 2005 36
10. ﺳﻮدﻣﻨﺪ ﻫﺎي ﻋﺎﻣﻞﺳﻮدﻣﻨﺪ ﻫﺎي ﻋﺎﻣﻞﺳﻮدﻣﻨﺪ ﻫﺎي ﻋﺎﻣﻞﺳﻮدﻣﻨﺪ ﻫﺎي ﻋﺎﻣﻞ
•درﺑﺴﻴﺎريازﻣﺤﻴﻂﻫﺎاﻫﺪافﺑﺮايﺗﻮﻟﻴﺪرﻓﺘﺎريﺑﺎﻛﻴﻔﻴﺖﺑﺎﻻﻣﻨﺎﺳﺐ
ﻧﻴﺴﺘﻨﺪ
•ﻣﺜﺎل:ﺗﺎﻛﺴﻲاﺗﻮﻣﺎﺗﻴﻚ
–ﻣﻤﻜﻦاﺳﺖﭼﻨﺪﻳﻦﻣﺴﻴﺮﺑﺮايرﺳﻴﺪنﺑﻪﻣﻘﺼﺪﻣﻮﺟﻮد،ﺑﺎﺷﺪاﻣﺎﺑﻌﻀﻲازآﻧﻬﺎ ﻦﻳﻦ ﭼﻴﺮﺑﺮﻴ رﺑﻮ ﻮﺑﻲ ﺑزﻬ
،ﺳﺮﻳﻌﺘﺮاﻣﻦ،ﺗﺮﻣﻄﻤﺌﻦﺗﺮوﻳﺎارزاﻧﺘﺮازﺑﻘﻴﻪﻣﻲﺑﺎﺷﻨﺪ
•اﻫﺪافﻣﻼﻛﻲﺧﺎمﺑﺮايﺗﻮﺻﻴﻒوﺿﻌﻴﺖﻫﺎﻫﺴﺘﻨﺪ)ﻣﻄﻠﻮبوﻧﺎﻣﻄﻠﻮب( م
•ﺗﺎﺑﻊﺳﻮدﻣﻨﺪي:ﺣﺎﻟﺖ)ﻳﺎدﻧﺒﺎﻟﻪايازﺣﺎﻻت(راﺑﻪﻳﻚﻋﺪدﺣﻘﻴﻘﻲ
ﻧﮕﺎﺷﺖﻣﻲﻛﻨﺪﻛﻪدرﺟﻪﻣﻄﻠﻮﺑﻴﺖآنراﺗﻮﺻﻴﻒﻣﻲﻛﻨﺪ
•اﻣﻜﺎنﺗﺼﻤﻴﻢﮔﻴﺮيدرﻣﻮارديﻛﻪ:
–اﻫﺪافﻣﺘﻨﺎﻗﺾﺑﺎﺷﻨﺪ ﺾﺑ
–ﭼﻨﺪﻳﻦﻫﺪفوﺟﻮدداردوﻟﻲرﺳﻴﺪنﺑﻪﻫﻴﭻﻳﻚﻗﻄﻌﻲﻧﻴﺴﺖ
N. Razavi- AI course- 2005 37
ﺳﻮدﻣﻨﺪي ﺑﺮ ﻣﺒﺘﻨﻲ ﻫﺎي ﻋﺎﻣﻞﺳﻮدﻣﻨﺪي ﺑﺮ ﻣﺒﺘﻨﻲ ﻫﺎي ﻋﺎﻣﻞي ﻮ ﺑﺮ ﻲ ﺒ ي ﻞي ﻮ ﺑﺮ ﻲ ﺒ ي ﻞ
Sensors
What the world
Sensors
State
H th ld l
E
What the world
is like now
How the world evolves
What it will be like if
nviron
What my actions do
What it will be like if
I do action A
nmen
Utility
How happy I will be
in such a state
t
What action I
should do now
Agent
Actuators
N. Razavi- AI course- 2005 38
ﻳﺎدﮔﻴﺮﻧﺪه ﻫﺎي ﻋﺎﻣﻞﻳﺎدﮔﻴﺮﻧﺪه ﻫﺎي ﻋﺎﻣﻞﻳﺎدﮔﻴﺮﻧﺪه ﻫﺎي ﻋﺎﻣﻞﻳﺎدﮔﻴﺮﻧﺪه ﻫﺎي ﻋﺎﻣﻞ
•ﺗﻮرﻳﻨﮓ)1950(:اﻳﺪهﺑﺮﻧﺎﻣﻪﻧﻮﻳﺴﻲواﻗﻌﻲﻫﻮﺷﻤﻨﺪﺑﻪﺻﻮرتدﺳﺘﻲ
ﻧﻴﺎزﺑﻪروشﻫﺎيﺳﺮﻳﻌﺘﺮ ش
ﺳﺎﺧﺖﻣﺎﺷﻴﻦﻫﺎيﻳﺎدﮔﻴﺮﻧﺪهوآﻣﻮزشﺑﻪآﻧﻬﺎ
•ﻣﻮﻟﻔﻪﻫﺎيﻋﺎﻣﻞﻳﺎدﮔﻴﺮﻧﺪه
–ﻋﻨﺼﺮﻳﺎدﮔﻴﺮﻧﺪه:ﺑﺮاياﻳﺠﺎدﺑﻬﺒﻮد
–ﻋﻨﺼﺮﻛﺎرآﻳﻲ:اﻧﺘﺨﺎبﻓﻌﺎﻟﻴﺖﻫﺎيﺧﺎرﺟﻲ
ﻘﻟﺎﺎا ﺎ اآ ﻛﺎاﮔ ﺎ –ﻣﻨﺘﻘﺪ:ﺗﻮﻟﻴﺪﺑﺎزﺧﻮردﺑﺎﺗﻮﺟﻪﺑﻪاﺳﺘﺎﻧﺪاردﻛﺎرآﻳﻲﺑﺮايﻋﻨﺼﺮﻳﺎدﮔﻴﺮﻧﺪه
–ﻣﻮﻟﺪﻣﺴﺎﻟﻪ:ﭘﻴﺸﻨﻬﺎدﻓﻌﺎﻟﻴﺖﻫﺎياﻛﺘﺸﺎﻓﻲ
•ﺜﺎل:ﺗﺎﻛﻚ ﺎﺗ اﺗ •ﻣﺜﺎل:ﺗﺎﻛﺴﻲاﺗﻮﻣﺎﺗﻴﻚ
–ﻋﻨﺼﺮﻛﺎرآﻳﻲ:ﺣﺮﻛﺖﺳﺮﻳﻊازﺧﻂ3ﺑﻪﺧﻂ1
–ﻣﻨﺘﻘﺪ:درﻳﺎﻓﺖﺷﻜﺎﻳﺖراﻧﻨﺪهﻫﺎيدﻳﮕﺮ ﻣﻨﺘﻘﺪ:درﻳﺎﻓﺖﺷﻜﺎﻳﺖراﻧﻨﺪهﻫﺎيدﻳﮕﺮ
–اﻳﺠﺎدﻗﺎﻧﻮﻧﻲﺑﻴﺎﻧﮕﺮﺑﺪﺑﻮدناﻳﻦﻋﻤﻞواﺻﻼحﻋﻨﺼﺮﻛﺎرآﻳﻲ
N. Razavi- AI course- 2005 39
ﻳﺎدﮔﻴﺮﻧﺪه ﻫﺎي ﻋﺎﻣﻞﻳﺎدﮔﻴﺮﻧﺪه ﻫﺎي ﻋﺎﻣﻞﻳﺎدﮔﻴﺮﻧﺪه ﻫﺎي ﻋﺎﻣﻞﻳﺎدﮔﻴﺮﻧﺪه ﻫﺎي ﻋﺎﻣﻞ
•اﻧﻮاعداﻧﺸﻲﻛﻪﻋﻨﺼﺮﻳﺎدﮔﻴﺮﻧﺪهﻣﻲﺗﻮاﻧﺪﻳﺎدﺑﮕﻴﺮد:
ﮔ ﺎﻘاﺎﻟاﻛ ا –ﻳﺎدﮔﻴﺮيﻣﺴﺘﻘﻴﻢازدﻧﺒﺎﻟﻪادراﻛﻲ
–ﻳﺎدﮔﻴﺮيﻧﺤﻮهﺗﻐﻴﻴﺮاتدﻧﻴﺎ:ﻣﺸﺎﻫﺪهدوﺣﺎﻟﺖﻣﺘﻮاﻟﻲ ﻴﺮي ﻳﻮﻴﻴﺮﻴوﻲ ﻮ
–ﻳﺎدﮔﻴﺮيدرﻣﻮردﺗﺎﺛﻴﺮﻋﻤﻞﻋﺎﻣﻞ:ﻣﺸﺎﻫﺪهﻧﺘﺎﻳﺞﻓﻌﺎﻟﻴﺖﻋﺎﻣﻞ
ﺎلﻛﺎﺎ •ﻣﺜﺎل:ﻧﺤﻮهﺗﺮﻣﺰﻛﺮدندرﺟﺎدهﻫﺎيﺧﻴﺲ
•ﭘﺎداشوﺟﺮﻳﻤﻪ
N. Razavi- AI course- 2005 40
11. ﻳﺎدﮔﻴﺮي ﻫﺎي ﻋﺎﻣﻞﻳﺎدﮔﻴﺮي ﻫﺎي ﻋﺎﻣﻞ
P f t d d
S
Performance standard
SensorsCritic
E
feedback
Enviro
Performance
l t
Learning
l t
changes
onmen
elementelement
knowledge
learning
goals
nt
Problem
generator
goals
Agent Actuators
N. Razavi- AI course- 2005 41