What changed ?
The kind of symbolic AI described in this book went through several cycles of hype and disappointment to the point where many think it is obsolete. People often do it connect recent breakthroughs in SAT and SMT solvers with this history and for that matter production rules engines are dramatically better than they were in the 1980s but they’ve never made a breakthrough into general purpose use.
Rule-based expert systems (as opposed to SAT, etc) based on Symbolic AI also have the issue that for non-trivial problems, coding the rules themselves usually requires programming expertise in addition to the domain knowledge required to capture the business logic. Things have improved a lot since the 80s, but applications remain fairly niche.
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.
My AI prof joked, I think, that it was "things that don't work yet" - clearly only a humanlike AI could do OCR... until it started working, etc.
But I actually think your hypothetically proposed definition fits the history even better.