▁The ▁content ment ▁function ▁maps ▁from ▁the ▁agent ' s ▁current ▁belief s , ▁or ▁anticip ated ▁belief s , ▁to ▁an ▁evaluation ▁of ▁the ▁utility ▁of ▁being ▁in ▁that ▁state . ▁It ▁is ▁trained ▁by ▁rein for cement ▁from ▁a ▁human ▁teacher . ▁In ▁order ▁to ▁facil itate ▁this ▁rein for cement ▁learning , ▁M AN IC ▁provides ▁a ▁mechanism ▁for ▁the ▁agent ▁to ▁generate ▁" f ant asy ▁videos " ▁that ▁show ▁anticip ated ▁observations ▁if ▁a ▁candidate ▁plan ▁were ▁to ▁be ▁executed . ▁The ▁idea ▁is ▁that ▁a ▁human ▁teacher ▁would ▁evaluate ▁these ▁videos ▁and ▁rank ▁them ▁according ▁to ▁des ira b ility ▁or ▁utility , ▁and ▁the ▁agent ▁could ▁then ▁use ▁that ▁feedback ▁to ▁ref ine ▁its ▁content ment ▁function . ▁ ▁Sent ience ▁M AN IC ▁propos es ▁that ▁the ▁learning ▁system ▁gives ▁the ▁agent ▁aw aren ess ▁of ▁its ▁environment ▁by ▁model ing ▁it , ▁and ▁using ▁that ▁model ▁to ▁anticip ate ▁future ▁belief s . ▁It ▁further ▁propos es ▁that ▁a ▁similar ▁mechanism ▁can ▁also ▁implement ▁sent ience . ▁That ▁is , ▁it ▁claims ▁that ▁aw aren ess ▁can ▁be ▁implemented ▁with ▁an ▁out ward - looking ▁model , ▁and ▁sent ience ▁can ▁be ▁implemented ▁with ▁an ▁in ward - looking ▁model . ▁Therefore , ▁it ▁propos es ▁to ▁add ▁" int ros pective ▁sens es ", ▁which ▁theoret ically ▁give ▁the ▁agent ▁the ▁ability ▁to ▁become ▁aware ▁of ▁its ▁own ▁inner ▁feelings , ▁by ▁model ing ▁them , ▁just ▁as ▁it ▁is ▁aware ▁of ▁its