IC ▁describes ▁a ▁software ▁agent ▁that , ▁supposed ly , ▁will ▁exhib it ▁cogn itive ▁intelligence . ▁The ▁agent ' s ▁artificial ▁brain ▁compr ises ▁two ▁major ▁components : ▁a ▁learning ▁system ▁and ▁a ▁decision - making ▁system . ▁ ▁Learning ▁system ▁The ▁learning ▁system ▁models ▁the ▁agent ' s ▁environment ▁as ▁a ▁dynam ical ▁system . ▁It ▁consists ▁of ▁an ▁" observ ation ▁function ", ▁which ▁maps ▁from ▁the ▁agent ' s ▁current ▁belief s ▁to ▁predicted ▁observations , ▁and ▁a ▁" transition ▁function ", ▁which ▁maps ▁from ▁current ▁belief s ▁to ▁future ▁belief s ▁in ▁the ▁next ▁time - step . ▁The ▁observation ▁function ▁is ▁implemented ▁with ▁a ▁gener ative ▁deep ▁learning ▁architecture . ▁It ▁is ▁trained ▁in ▁an ▁un super vised ▁manner ▁from ▁the ▁observations ▁that ▁the ▁agent ▁makes . ▁The ▁intr insic ▁representations ▁of ▁those ▁observations ▁become ▁the ▁agents ▁" bel ief s ". ▁The ▁transition ▁function ▁is ▁trained ▁in ▁a ▁super vised ▁manner , ▁to ▁predict ▁the ▁next ▁belief s ▁from ▁the ▁current ▁ones . ▁The ▁entire ▁learning ▁system ▁is ▁based ▁lo os ely ▁on ▁a ▁ 2 0 1 1 ▁paper ▁by ▁Michael ▁S . ▁G ash ler ▁that ▁describes ▁a ▁method ▁for ▁training ▁a ▁deep ▁neural ▁network ▁to ▁model ▁a ▁simple ▁dynam ical ▁system ▁from ▁visual ▁observations . ▁ ▁Dec ision - making ▁system ▁The ▁decision - making ▁system ▁consists ▁of ▁a ▁planning ▁module ▁and ▁a ▁content ment ▁function . ▁The ▁planning ▁module ▁uses ▁an ▁evolution ary ▁algorithm ▁to ▁evol ve ▁a ▁satisf icing ▁plan .