AI Slide Farsi

1. ‫ﻫﻮﺷﻤﻨﺪ‬ ‫ﻫﺎي‬ ‫ﻋﺎﻣﻞ‬‫ﻫﻮﺷﻤﻨﺪ‬ ‫ﻫﺎي‬ ‫ﻋﺎﻣﻞ‬‫ﻮ‬ ‫ي‬ ‫ﻞ‬‫ﻮ‬ ‫ي‬ ‫ﻞ‬
‫دوم‬ ‫ﻓﺼﻞ‬
‫رﺿﻮي‬ ‫ﻧﺎﺻﺮ‬ ‫ﺳﻴﺪ‬‫رﺿﻮي‬ ‫ﺻﺮ‬ ‫ﻴ‬
Email: razavi@Comp.iust.ac.ir
13841384
‫ﻣﻄﺎﻟﺐ‬ ‫رﺋﻮس‬‫ﻣﻄﺎﻟﺐ‬ ‫رﺋﻮس‬‫ﻣﻄﺎﻟﺐ‬ ‫رﺋﻮس‬‫ﻣﻄﺎﻟﺐ‬ ‫رﺋﻮس‬
•‫ﻫﺎ‬ ‫ﻣﺤﻴﻂ‬ ‫و‬ ‫ﻫﺎ‬ ‫ﻋﺎﻣﻞ‬
‫ﻄ‬ •‫ﺑﻮدن‬ ‫ﻣﻨﻄﻘﻲ‬
•PEAS)‫ﻫﺎ‬ ‫ﮕ‬ ‫ﺣ‬ ،‫ﻫﺎ‬ ‫ﻛﻨﻨﺪه‬ ‫اﺛ‬ ،‫ﻂ‬ ‫ﻣﺤ‬ ، ‫ﻛﺎرآﻳ‬ ‫ﺎر‬ ‫ﻣﻌ‬( •PEAS)‫ﺣﺴﮕﺮﻫﺎ‬ ،‫ﻫﺎ‬ ‫اﺛﺮﻛﻨﻨﺪه‬ ،‫ﻣﺤﻴﻂ‬ ،‫ﻛﺎرآﻳﻲ‬ ‫ﻣﻌﻴﺎر‬(
•‫ﻫﺎ‬ ‫ﻣﺤﻴﻂ‬ ‫اﻧﻮاع‬‫ع‬
•‫ﻫﺎ‬ ‫ﻋﺎﻣﻞ‬ ‫اﻧﻮاع‬
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‫ﻫﺎ‬ ‫ﻋﺎﻣﻞ‬‫ﻫﺎ‬ ‫ﻋﺎﻣﻞ‬‫ﻫﺎ‬ ‫ﻋﺎﻣﻞ‬‫ﻫﺎ‬ ‫ﻋﺎﻣﻞ‬
•‫ﻋﺎﻣﻞ‬:‫ﻫﺮ‬‫ﭼﻴﺰي‬‫ﻛﻪ‬‫ﺑﺘﻮاﻧﺪ‬‫ﻣﺤﻴﻂ‬‫ﭘﻴﺮاﻣﻮﻧﺶ‬‫را‬‫از‬‫ﻃﺮﻳﻖ‬‫ﺣﺴﮕﺮﻫﺎ‬
‫ك‬‫ﻛﻨﺪ‬‫آن‬‫ﻂ‬‫از‬‫ﻖ‬ ‫ﻃ‬‫ﻛﻨﻨﺪ‬ ‫اﺛ‬‫ﺎ‬‫ﻞ‬‫ﻛﻨﺪ‬ ‫درك‬‫ﻛﻨﺪ‬‫و‬‫در‬‫آن‬‫ﻣﺤﻴﻂ‬‫از‬‫ﻃﺮﻳﻖ‬‫اﺛﺮﻛﻨﻨﺪه‬‫ﻫﺎ‬‫ﻋﻤﻞ‬‫ﻛﻨﺪ‬.
•‫ﻋﺎﻣﻞ‬‫اﻧﺴﺎﻧﻲ‬: ‫ﻞ‬‫ﻲ‬
–‫ﭼﺸﻢ‬‫ﻫﺎ‬‫و‬‫ﮔﻮش‬‫ﻫﺎ‬‫و‬‫ﺳﺎﻳﺮ‬‫اﻧﺪام‬‫ﻫﺎي‬‫ﺣﺴﻲ‬‫ﺑﻪ‬‫ﻋﻨﻮان‬‫ﺣﺴﮕﺮﻫﺎ‬
–‫دﺳﺖ‬،‫ﻫﺎ‬،‫ﭘﺎﻫﺎ‬‫دﻫﺎن‬‫و‬‫ﺳﺎﻳﺮ‬‫اﻋﻀﺎي‬‫ﺑﺪن‬‫ﺑﻪ‬‫ﻋﻨﻮان‬‫اﺛﺮ‬‫ﻛﻨﻨﺪه‬‫ﻫﺎ‬
•‫ﻋﺎﻣﻞ‬‫روﺑﺎت‬: ‫ﻋﺎﻣﻞ‬‫روﺑﺎت‬:
–‫دورﺑﻴﻦ‬‫ﻫﺎ‬‫و‬‫ﻓﺎﺻﻠﻪ‬‫ﻳﺎب‬‫ﻣﺎدون‬‫ﻗﺮﻣﺰ‬‫ﺑﻪ‬‫ﻋﻨﻮان‬‫ﺣﺴﮕﺮﻫﺎ‬
–‫اﻧﻮاع‬‫ﻣﻮﺗﻮرﻫﺎ‬‫ﺑﻪ‬‫ﻋﻨﻮان‬‫اﺛﺮ‬‫ﻛﻨﻨﺪه‬‫ﻫﺎ‬
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‫ﻫﺎ‬ ‫ﻣﺤﻴﻂ‬ ‫و‬ ‫ﻫﺎ‬ ‫ﻋﺎﻣﻞ‬‫ﻫﺎ‬ ‫ﻣﺤﻴﻂ‬ ‫و‬ ‫ﻫﺎ‬ ‫ﻋﺎﻣﻞ‬‫ﻫﺎ‬ ‫ﻣﺤﻴﻂ‬ ‫و‬ ‫ﻫﺎ‬ ‫ﻋﺎﻣﻞ‬‫ﻫﺎ‬ ‫ﻣﺤﻴﻂ‬ ‫و‬ ‫ﻫﺎ‬ ‫ﻋﺎﻣﻞ‬
•‫ﻋﺎﻣﻞ‬ ‫ﺗﺎﺑﻊ‬‫ﻛﻨﺪ‬ ‫ﻣﻲ‬ ‫ﻧﮕﺎﺷﺖ‬ ‫اﻋﻤﺎل‬ ‫ﺑﻪ‬ ‫را‬ ‫ادراﻛﻲ‬ ‫ﺗﺎرﻳﺨﭽﻪ‬: ‫ﻞ‬ ‫ﻊ‬
[f: P* A]
•‫ﻋﺎﻣﻞ‬ ‫ﺑﺮﻧﺎﻣﻪ‬‫اﻳﺠﺎد‬ ‫ﺑﺮاي‬f‫روي‬ ‫ﺑﺮ‬‫ﻣﻌﻤﺎري‬‫ﺷﻮد‬ ‫ﻣﻲ‬ ‫اﺟﺮا‬ ‫ﻓﻴﺰﻳﻜﻲ‬.
•‫ﻞ‬ ‫ﺎ‬‫ﺎ‬‫ﻧﺎ‬ •‫ﻋﺎﻣﻞ‬=‫ﻣﻌﻤﺎري‬+‫ﺑﺮﻧﺎﻣﻪ‬
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2. ‫ﺑﺮﻗ‬ ‫ﺟﺎرو‬ ‫دﻧﻴﺎي‬‫ﺑﺮﻗ‬ ‫ﺟﺎرو‬ ‫دﻧﻴﺎي‬‫ﺑﺮﻗﻲ‬ ‫ﺟﺎرو‬ ‫دﻧﻴﺎي‬‫ﺑﺮﻗﻲ‬ ‫ﺟﺎرو‬ ‫دﻧﻴﺎي‬
•‫ﻫﺎ‬ ‫ادراك‬:‫ﻣﺎﻧﻨﺪ‬ ،‫آﻧﻬﺎ‬ ‫ﻣﺤﺘﻮﻳﺎت‬ ‫و‬ ‫ﻫﺎ‬ ‫ﻣﻜﺎن‬[A, Dirty]
•‫اﻋﻤﺎل‬:‫و‬ ‫ﻣﻜﺶ‬ ،‫راﺳﺖ‬ ‫و‬ ‫ﭼﭗ‬ ‫ﺑﻪ‬ ‫ﺣﺮﻛﺖ‬NoOp
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‫ﻣﻨﻄﻘ‬ ‫ﻫﺎي‬ ‫ﻋﺎﻣﻞ‬‫ﻣﻨﻄﻘ‬ ‫ﻫﺎي‬ ‫ﻋﺎﻣﻞ‬‫ﻣﻨﻄﻘﻲ‬ ‫ﻫﺎي‬ ‫ﻋﺎﻣﻞ‬‫ﻣﻨﻄﻘﻲ‬ ‫ﻫﺎي‬ ‫ﻋﺎﻣﻞ‬
•‫ﺗﻮاﻧﺪ‬ ‫ﻣﻲ‬ ‫ﻛﻪ‬ ‫اﻋﻤﺎﻟﻲ‬ ‫و‬ ‫ﻛﻨﺪ‬ ‫درك‬ ‫ﺗﻮاﻧﺪ‬ ‫ﻣﻲ‬ ‫ﻛﻪ‬ ‫آﻧﭽﻪ‬ ‫اﺳﺎس‬ ‫ﺑﺮ‬ ‫ﺑﺎﻳﺪ‬ ‫ﻋﺎﻣﻞ‬ ‫ﻳﻚ‬
،‫دﻫﺪ‬ ‫اﻧﺠﺎم‬»‫دﻫﺪ‬ ‫اﻧﺠﺎم‬ ‫را‬ ‫درﺳﺖ‬ ‫ﻛﺎر‬«‫ﺷﻮد‬ ‫ﺑﺎﻋﺚ‬ ‫ﻛﻪ‬ ‫اﺳﺖ‬ ‫آن‬ ‫درﺳﺖ‬ ‫ﻋﻤﻞ‬ ،‫دﻫﺪ‬ ‫اﻧﺠﺎم‬»‫دﻫﺪ‬ ‫اﻧﺠﺎم‬ ‫را‬ ‫درﺳﺖ‬ ‫ﻛﺎر‬«.‫ﺷﻮد‬ ‫ﺑﺎﻋﺚ‬ ‫ﻛﻪ‬ ‫اﺳﺖ‬ ‫آن‬ ‫درﺳﺖ‬ ‫ﻋﻤﻞ‬
‫آورد‬ ‫ﺑﺪﺳﺖ‬ ‫را‬ ‫ﻣﻮﻓﻘﻴﺖ‬ ‫ﺑﻴﺸﺘﺮﻳﻦ‬ ‫ﻋﺎﻣﻞ‬.
‫آ‬ ‫ﻛ‬‫ﻚ‬ ‫ﻚ‬ •‫ﻛﺎرآﻳﻲ‬ ‫ﻣﻌﻴﺎر‬:‫ﻋﺎﻣﻞ‬ ‫ﻳﻚ‬ ‫رﻓﺘﺎر‬ ‫ﻣﻮﻓﻘﻴﺖ‬ ‫ﻣﻴﺰان‬ ‫ﺳﻨﺠﺶ‬ ‫ﺑﺮاي‬ ‫ﻫﺪف‬ ‫ﻣﻌﻴﺎر‬ ‫ﻳﻚ‬
•‫ﻣﺜﺎل‬:‫ﺑﺮﻗﻲ‬ ‫ﺟﺎرو‬ ‫دﻧﻴﺎي‬ ‫ﻋﺎﻣﻞ‬ ‫ﻣﻮﻓﻘﻴﺖ‬ ‫ﻣﻌﻴﺎر‬: ‫ل‬‫ﻲ‬ ‫ﺑﺮ‬ ‫رو‬ ‫ﺟ‬ ‫ي‬ ‫ﻴ‬ ‫ﻞ‬ ‫ﻴ‬ ‫ﻮ‬ ‫ر‬ ‫ﻴ‬
–‫ﺷﺪه‬ ‫ﺗﻤﻴﺰ‬ ‫ﺧﺎك‬ ‫و‬ ‫ﮔﺮد‬ ‫ﻣﻘﺪار‬
‫ﺷﺪ‬ ‫ف‬ ‫ﺎن‬ ‫ز‬ ‫ﺰان‬ –‫ﺷﺪه‬ ‫ﻣﺼﺮف‬ ‫زﻣﺎن‬ ‫ﻣﻴﺰان‬
–‫ﺷﺪه‬ ‫ﻣﺼﺮف‬ ‫ﺑﺮق‬ ‫ﻣﻘﺪار‬
–‫و‬ ‫ﺷﺪه‬ ‫ﺗﻮﻟﻴﺪ‬ ‫ﺻﺪاي‬ ‫و‬ ‫ﺳﺮ‬ ‫ﻣﻴﺰان‬...
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‫ﻣﻨﻄﻘ‬ ‫ﻫﺎي‬ ‫ﻋﺎﻣﻞ‬‫ﻣﻨﻄﻘ‬ ‫ﻫﺎي‬ ‫ﻋﺎﻣﻞ‬‫ﻣﻨﻄﻘﻲ‬ ‫ﻫﺎي‬ ‫ﻋﺎﻣﻞ‬‫ﻣﻨﻄﻘﻲ‬ ‫ﻫﺎي‬ ‫ﻋﺎﻣﻞ‬
‫ﻄ‬‫ﻟ‬‫ﻛ‬‫ﻜ‬‫ﻚ‬‫ﻄ‬ •‫ﻋﺎﻣﻞ‬‫ﻣﻨﻄﻘﻲ‬:‫ﺑﺮاي‬‫ﻫﺮ‬‫دﻧﺒﺎﻟﻪ‬‫ادراﻛﻲ‬،‫ﻣﻤﻜﻦ‬‫ﻳﻚ‬‫ﻋﺎﻣﻞ‬‫ﻣﻨﻄﻘﻲ‬
‫ﺑﺎﻳﺪ‬‫ﺑﺮ‬‫اﺳﺎس‬‫ﺷﻮاﻫﺪ‬‫درﻳﺎﻓﺘﻲ‬‫از‬‫دﻧﺒﺎﻟﻪ‬‫ادراﻛﻲ‬‫و‬‫داﻧﺶ‬‫دروﻧﻲ‬ ‫ﻳ‬ ‫ﺑ‬‫ﺑﺮ‬‫س‬‫ﻮ‬‫ﻲ‬ ‫رﻳ‬‫ز‬‫ﺒ‬‫ﻲ‬ ‫ر‬‫و‬‫ﺶ‬‫ﻲ‬‫رو‬
‫ﻋﻤﻠﻲ‬‫را‬‫اﻧﺘﺨﺎب‬‫ﻛﻨﺪ‬‫ﻛﻪ‬‫اﻧﺘﻈﺎر‬‫ﻣﻲ‬‫رود‬‫ﻣﻌﻴﺎر‬‫ﻛﺎرآﻳﻲ‬‫اش‬‫را‬‫ﺑﻪ‬
‫ﺪاﻛﺜ‬‫ﺎﻧﺪ‬ ‫ﺣﺪاﻛﺜﺮ‬‫ﺑﺮﺳﺎﻧﺪ‬.
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‫ﻣﻨﻄﻘ‬ ‫ﻫﺎي‬ ‫ﻋﺎﻣﻞ‬‫ﻣﻨﻄﻘ‬ ‫ﻫﺎي‬ ‫ﻋﺎﻣﻞ‬‫ﻣﻨﻄﻘﻲ‬ ‫ﻫﺎي‬ ‫ﻋﺎﻣﻞ‬‫ﻣﻨﻄﻘﻲ‬ ‫ﻫﺎي‬ ‫ﻋﺎﻣﻞ‬
•‫ﻣﻨﻄﻘﻲ‬‫ﺑﻮدن‬‫ﺑﺎ‬‫داﻧﺶ‬‫ﻛﻞ‬‫ﺑﻮدن‬)‫داﻧﺴﺘﻦ‬‫ﻫﻤﻪ‬‫ﭼﻴﺰ‬‫ﺗﻮﺳﻂ‬‫داﻧﺶ‬
‫ﺪ‬ ‫ﻧﺎ‬(‫ت‬ ‫ﺗﻔﺎ‬‫ا‬ ‫ﻧﺎﻣﺤﺪود‬(‫ﺗﻔﺎوت‬‫دارد‬.
•‫ﻋﺎﻣﻞ‬‫ﻣﻲ‬‫ﺗﻮاﻧﺪ‬‫اﻋﻤﺎﻟﻲ‬‫را‬‫اﻧﺠﺎم‬‫دﻫﺪ‬‫ﻛﻪ‬‫از‬‫ﻃﺮﻳﻖ‬‫ﺗﻐﻴﻴﺮ‬‫در‬‫ادراك‬ ‫ﻞ‬‫ﻲ‬‫ﻮ‬‫ﻲ‬‫ر‬‫م‬ ‫ﺠ‬‫ز‬‫ﺮﻳﻖ‬‫ﻴﻴﺮ‬‫ر‬‫ر‬
‫ﻫﺎي‬‫آﺗﻲ‬‫اﻃﻼﻋﺎت‬‫ﻣﻔﻴﺪ‬‫ﺑﺪﺳﺖ‬‫آورد‬)‫ﺟﻤﻊ‬‫آوري‬،‫داﻧﺶ‬
‫ﺸﺎف‬ ‫اﻛ‬(‫اﻛﺘﺸﺎف‬(
•‫ﻳﻚ‬‫ﻋﺎﻣﻞ‬‫ﺧﻮدﻣﺨﺘﺎر‬‫اﺳﺖ‬‫اﮔﺮ‬‫رﻓﺘﺎرش‬‫ﺑﺮ‬‫اﺳﺎس‬‫ﺗﺠﺮﺑﻪ‬‫اش‬‫ﺗﻌﻴﻴﻦ‬ ‫ﻳﻚ‬‫ﻞ‬‫ر‬ ‫ﻮ‬‫ﺖ‬ ‫ا‬‫ﺮ‬ ‫ا‬‫رش‬ ‫ر‬‫ﺑﺮ‬‫س‬ ‫ا‬‫ﺠﺮﺑ‬‫اش‬‫ﻴﻴﻦ‬
‫ﺷﻮد‬)‫ﺑﻪ‬‫ﻫﻤﺮاه‬‫ﻗﺎﺑﻠﻴﺖ‬‫ﻳﺎدﮔﻴﺮي‬‫و‬‫ﺗﻄﺒﻴﻖ‬‫ﭘﺬﻳﺮي‬(
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3. PEASPEASPEASPEAS
• PEAS: Performance measure, Environment, Actuators, Sensors
•‫ا‬ ‫ﻃ‬‫ﻚ‬‫ﻞ‬ ‫ﺎ‬‫ﺪا‬ ‫ا‬‫ﺪ‬ ‫ﺎ‬‫ا‬‫ﺎﻻ‬‫ﺗ‬‫ﻧﺪ‬ ‫ﮔ‬ •‫در‬‫ﻃﺮاﺣﻲ‬‫ﻳﻚ‬‫ﻋﺎﻣﻞ‬‫اﺑﺘﺪا‬‫ﺑﺎﻳﺪ‬‫ﻣﻮارد‬‫ﺑﺎﻻ‬‫ﺗﻌﻴﻴﻦ‬‫ﮔﺮدﻧﺪ‬.
•‫ﻣﺜﺎل‬:‫ﻃﺮاﺣﻲ‬‫ﻳﻚ‬‫راﻧﻨﺪه‬‫ﺗﺎﻛﺴﻲ‬‫اﺗﻮﻣﺎﺗﻴﻚ‬ ‫ل‬:‫ﻲ‬ ‫ﺮا‬‫ﻳﻚ‬‫را‬‫ﻲ‬‫ﻴﻚ‬ ‫ﻮ‬ ‫ا‬
–‫ﻣﻌﻴﺎر‬‫ﻛﺎرآﻳﻲ‬:،‫اﻣﻨﻴﺖ‬،‫ﺳﺮﻋﺖ‬،‫راﺣﺘﻲ‬‫ﺳﻮد‬‫و‬...
–‫ﻣﺤﻴﻂ‬:‫ﺧﻴﺎﺑﺎن‬،‫ﻫﺎ‬‫اﻓﺮاد‬،‫ﭘﻴﺎده‬‫ﻣﺸﺘﺮي‬‫ﻫﺎ‬‫و‬...
–‫اﺛﺮﻛﻨﻨﺪه‬‫ﻫﺎ‬:،‫ﻓﺮﻣﺎن‬‫ﺷﺘﺎب‬،‫دﻫﻨﺪه‬،‫ﺗﺮﻣﺰﻫﺎ‬،‫ﺑﻮق‬‫ﭼﺮاغ‬‫ﻫﺎ‬‫و‬ ‫اﺛﺮﻛﻨﻨﺪه‬‫ﻫﺎ‬:،‫ﻓﺮﻣﺎن‬‫ﺷﺘﺎب‬،‫دﻫﻨﺪه‬،‫ﺗﺮﻣﺰﻫﺎ‬،‫ﺑﻮق‬‫ﭼﺮاغ‬‫ﻫﺎ‬‫و‬...
–‫ﺣﺴﮕﺮﻫﺎ‬:‫دورﺑﻴﻦ‬،‫ﻫﺎ‬‫ﺣﺴﮕﺮﻫﺎي‬‫ﺻﻮﺗﻲ‬(Sonar)،‫ﺳﺮﻋﺖ‬،‫ﺳﻨﺞ‬
‫ﮕ‬‫ﻜ‬ GPS،‫ﻛﻴﻠﻮﻣﺘﺮ‬،‫ﺷﻤﺎر‬‫ﺣﺴﮕﺮﻫﺎي‬،‫ﻣﻮﺗﻮر‬‫ﺻﻔﺤﻪ‬،‫ﻛﻠﻴﺪ‬‫ﻣﻴﻜﺮوﻓﻮن‬‫و‬...
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PEASPEASPEASPEAS
•‫ﻋﺎﻣﻞ‬:‫ﺳﻴﺴﺘﻢ‬‫ﺗﺸﺨﻴﺺ‬‫ﭘﺰﺷﻜﻲ‬
‫ﺎ‬‫آ‬ ‫ﻛﺎ‬‫ﻼ‬‫ﺎ‬‫اﻗﻞ‬‫ﺎ‬ –‫ﻣﻌﻴﺎر‬‫ﻛﺎرآﻳﻲ‬:‫ﺳﻼﻣﺘﻲ‬،‫ﺑﻴﻤﺎر‬‫ﺑﻪ‬‫ﺣﺪاﻗﻞ‬‫رﺳﺎﻧﺪن‬‫ﻫﺰﻳﻨﻪ‬‫و‬...
–‫ﻣﺤﻴﻂ‬:،‫ﺑﻴﻤﺎر‬،‫ﺑﻴﻤﺎرﺳﺘﺎن‬‫ﻛﺎرﻣﻨﺪان‬‫و‬... ‫ﻴ‬‫ر‬ ‫ﺑﻴ‬‫ر‬ ‫ﺑﻴ‬‫ر‬‫و‬
–‫اﺛﺮ‬‫ﻛﻨﻨﺪه‬‫ﻫﺎ‬:‫ﺻﻔﺤﻪ‬‫ﻧﻤﺎﻳﺶ‬)‫ﭘﺮﺳﺶ‬،‫ﻫﺎ‬‫آزﻣﺎﻳﺶ‬،‫ﻫﺎ‬‫ﺗﺸﺨﻴﺺ‬،‫ﻫﺎ‬
‫ا‬ ‫ﺪا‬(‫ﻣﺪاوا‬(
–‫ﺣﺴﮕﺮﻫﺎ‬:‫ﺻﻔﺤﻪ‬‫ﻛﻠﻴﺪ‬)‫درﻳﺎﻓﺖ‬،‫ﻋﻼﻳﻢ‬‫ﻳﺎﻓﺘﻪ‬‫ﻫﺎ‬‫و‬‫ﭘﺎﺳﺦ‬‫ﻫﺎي‬‫ﺑﻴﻤﺎر‬( ‫ﻢ‬‫ﺦ‬
N. Razavi- AI course- 2005 10
PEASPEASPEASPEAS
•‫ﻋﺎﻣﻞ‬:‫روﺑﺎت‬‫ﺟﺎﺑﻪ‬‫ﺟﺎ‬‫ﻛﻨﻨﺪه‬‫اﺷﻴﺎء‬
‫ﺎ‬‫آ‬ ‫ﻛﺎ‬‫ﺎ‬ ‫ﻗﻄ‬‫ﻛ‬‫ق‬‫ا‬ ‫ﻗ‬‫ﮔ‬ –‫ﻣﻌﻴﺎر‬‫ﻛﺎرآﻳﻲ‬:‫درﺻﺪ‬‫ﻗﻄﻌﺎﺗﻲ‬‫ﻛﻪ‬‫در‬‫ﺻﻨﺪوق‬‫درﺳﺖ‬‫ﻗﺮار‬‫ﻣﻲ‬‫ﮔﻴﺮﻧﺪ‬
–‫ﻣﺤﻴﻂ‬:‫ﻧﻮار‬‫ﻧﻘﺎﻟﻪ‬‫و‬‫اﺷﻴﺎء‬‫روي‬،‫آن‬‫ﺻﻨﺪوق‬‫ﻫﺎ‬ ‫ﻴ‬‫ر‬ ‫ﻮ‬‫و‬‫ﻴ‬‫روي‬‫و‬
–‫اﺛﺮﻛﻨﻨﺪه‬‫ﻫﺎ‬:‫ﺑﺎزوﻫﺎ‬‫و‬‫دﺳﺖ‬
‫ﮕ‬‫ﮕ‬ –‫ﺣﺴﮕﺮﻫﺎ‬:،‫دورﺑﻴﻦ‬‫ﺣﺴﮕﺮ‬‫زاوﻳﻪ‬‫ﻣﻔﺎﺻﻞ‬
N. Razavi- AI course- 2005 11
PEASPEASPEASPEAS
•‫آﻣﻮزش‬‫دﻫﻨﺪه‬‫زﺑﺎن‬‫ﺑﻪ‬‫ﺻﻮرت‬‫ﻣﺤﺎوره‬‫اي‬
‫ﺎ‬‫آ‬ ‫ﻛﺎ‬‫اﻛ‬‫ﺎ‬‫ا‬‫آ‬‫ﺎ‬ ‫ا‬ –‫ﻣﻌﻴﺎر‬‫ﻛﺎرآﻳﻲ‬:‫ﺑﻪ‬‫ﺣﺪاﻛﺜﺮ‬‫رﺳﺎﻧﺪن‬‫ﻧﻤﺮه‬‫داﻧﺶ‬‫آﻣﻮز‬‫در‬‫اﻣﺘﺤﺎن‬
–‫ﻣﺤﻴﻂ‬:‫ﻣﺠﻤﻮﻋﻪ‬‫داﻧﺶ‬‫آﻣﻮزان‬ ‫ﻴ‬‫ﻮ‬‫ﺶ‬‫ﻮز‬
–‫اﺛﺮﻛﻨﻨﺪه‬‫ﻫﺎ‬:‫ﺻﻔﺤﻪ‬‫ﻧﻤﺎﻳﺶ‬)‫ﺗﻤﺮﻳﻦ‬،‫ﻫﺎ‬‫ﭘﻴﺸﻨﻬﺎدات‬‫و‬‫اﺻﻼﺣﺎت‬(
‫ﮕ‬ –‫ﺣﺴﮕﺮﻫﺎ‬:‫ﺻﻔﺤﻪ‬‫ﻛﻠﻴﺪ‬
N. Razavi- AI course- 2005 12

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