Any tips for introductory/intermediate material on the topic? In undergrad, we used Russel and Norvig. Surely there are more advanced books or courses.
That being said, the field of Operations Research, at least philosophically speaking, has picked up where AI researchers dropped it off. They've fully embraced the idea that human experts can model many systems extremely well, and have built incredible tools to do so: mathematical modeling and optimization, constraint programming, boolsat, graphical models, etc. Effectively speaking, if you think expert systems are cool, the next logical step is to delve into constraint programming, which is a sort of evolution of logic programming. I'd recommend the Minizinc Tutorial for a practical introduction with a nice DSL. Constraint Processing by Rina Dechter is a great intro with a more academic bent. I'd say mathematical modeling has been Ops Research's greatest success, and I'd definitely recommend Model Building with Mathematical Programming by Paul Williams.
I'd also say that Baysians have embraced these ideas (that of building upon human expertise) within the field of Machine Learning far more than other ML researchers have. My recommendations here are probably less helpful...I've only ever toyed with Bayesian learning models, but never employed them professionally. But I would recommend Doing Bayesian Data Analysis by Kruschke. It was very helpful as an introductory material.
These are in widespread use for a few applications. Probably every bank has at least one iLog instance running for enforcing business rules. Another one is "complex event processing", where a RETE engine makes it easy to aggregate small events into larger events. Also, a few efforts, such as Inform7 and Clara have tried to push the boundaries of programming for non-experts and real-life applications.
It is interesting, however, that the technology is not further applied, and a deep analysis of that could be worthwhile. For instance, RETE networks can eat "callback hell" situations for lunch, much like the complex event processing case. Instead, however, we are seeing one awful Javascript framework after another, and coroutines pushed as a very narrow answer to the problems on the server.
Note that all forms of A.I. have ties to optimization. For instance, usually when you train a neural net you are minimizing some kind of an error function. Drools (and iLog) both have optimization frameworks, etc.
The recent discussion of "superintelligence" has been marked by both a lack of imagination and any awareness of previous work on the subject. For instance, Rules engines are a fairly direct answer to "AI Safety" and "AI Ethics" problems that there is so much handwringing about. Most areas that require computers to be "creative" amount to some kind of multi-objective optimization, and even if rules can't make a system good, they can at least prevent the worst abuses.
I likewise find it sad that the idea has died so much that we continue to build square wheels that were made obsolete by rule engines. I remember a futile attempt at Amazon to get a team to scrap their system in favor of a RETE based rule engine. Theirs was a poorly performing and fragile homemade "rule engine" that compiled xml rules into if/else statements, which after tens of thousands of rules had slowed to a snails pace. Somehow the magic of O(1) escaped them, and they practically required me to reimplement their system from scratch in order to convince them. So I let it go.
I had never considered the possibility of killing callback hell with a rules engine but I can definitely see it now. I personally have toyed with building a compiler that eschews the pipeline architecture with a rules engine where both analyses and optimizations are implemented as rules. I definitely think there is a world of possibilities out there, but maybe we'll need another AI winter before people consider them again.
ps: this book https://www.amazon.ca/Prolog-Programming-Artificial-Intellig...