I think ideas from symbolic AI are relevant here and there but they certainly aren't fashionable.
Almost any financial institution has a copy of IBM iLOG in there somewhere implementing policy in terms of production rules.
Some of the most interesting systems today combine ideas from machine learning with ideas from AI search. For instance there are many game playing programs like AlphaGo that use
which runs a large number of games to the end rather than searching the next few moves exhaustively. Using a machine learning model to play the game for the playouts but sampling a large number of moves with A.I. search turns out to be a winning strategy.
Almost any financial institution has a copy of IBM iLOG in there somewhere implementing policy in terms of production rules.
Some of the most interesting systems today combine ideas from machine learning with ideas from AI search. For instance there are many game playing programs like AlphaGo that use
https://en.wikipedia.org/wiki/Monte_Carlo_tree_search
which runs a large number of games to the end rather than searching the next few moves exhaustively. Using a machine learning model to play the game for the playouts but sampling a large number of moves with A.I. search turns out to be a winning strategy.