 Eventually the earliest successful expert systems , such as XCON , proved too expensive to maintain . They were difficult to update , they could not learn , they were " brittle " ( i.e. , they could make grotesque mistakes when given unusual inputs ) , and they fell prey to problems ( such as the qualification problem ) that had been identified years earlier . Expert systems proved useful , but only in a few special contexts . 
 Eventually the earliest successful expert systems , such as XCON , proved too expensive to maintain . They <a href='uninflect' title='1'><mark>were</mark></a> difficult to update , they could not learn , they <a href='uninflect' title='2'><mark>were</mark></a> " brittle " ( i.e. , they could make grotesque mistakes when given unusual inputs ) , and they fell prey to problems ( such as the qualification problem ) <a href='null_relcl' title='3'><mark>that</mark></a><a href='been_done' title='4'><mark>had</mark></a> been identified years earlier . Expert systems proved useful , but only in a few special contexts . 
Eventually the earliest successful expert systems , such as XCON , proved too expensive to maintain . They was difficult to update , they could not learn , they was " brittle " ( i.e. , they could make grotesque mistakes when given unusual inputs ) , and they fell prey to problems ( such as the qualification problem ) done been identified years earlier . Expert systems proved useful , but only in a few special contexts .