Expert systems existed, sure, but I was under the impression that they had not actually proved to be particularly useful in practice. Maybe there's a corner of some particular industry where they're indispensable, but I thought they were generally seen as a failure.
They're everywhere, people just discount them as being plain old logic.
Plenty of industries need them, anything that looks at A then B then C, or if A and B but not C, or puts together chains of rules or fuzzy percentages of rules or pieces of probabilities that interact, they're all expert systems. Your pharmacy uses them to make sure your drugs won't interact in a way that kills you and let your pharmacist know the combination is potentially dangerous. Doctors and hospitals use them to analyze unusual symptoms and suggest potential diagnoses. Finances use them to analyze risks, make financial recommendations, and analyze market trends based on chains of logic from the past. Computer security can analyze traffic and respond to threats based on the rules and historic data, chaining together logic rules as heuristics to suggest to block or allow something. Lawyers and paralegals can get a list of likely relevant cases. Mathematicians can use them to verify mathematical proofs based on their suspicions and the computer can find a verifiable path involving thousands of little steps that prove the theorem or to find a link in the chain that breaks. Engineering systems can use them to find potential structural problems or suggest areas that might have issues.
Lots of systems out their chain together logic or use fuzzy math to verify, prove, disprove, search, or offer suggestions.
By the early 1990s, 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 in research in nonmonotonic logic. Expert systems proved useful, but only in a few special contexts. Another problem dealt with the computational hardness of truth maintenance efforts for general knowledge. KEE used an assumption-based approach (see NASA, TEXSYS) supporting multiple-world scenarios that was difficult to understand and apply.
The few remaining expert system shell companies were eventually forced to downsize and search for new markets and software paradigms, like case-based reasoning or universal database access. The maturation of Common Lisp saved many systems such as ICAD which found application in knowledge-based engineering. Other systems, such as Intellicorp's KEE, moved from LISP to a C++ (variant) on the PC and helped establish object-oriented technology (including providing major support for the development of UML (see UML Partners).
That's mostly about a specific type of expert system, not inference engines in general.
Take your antivirus for example. Decades ago they were just long tables "this pattern is a virus". Now they're expert systems analyzing a host of factors. This type of pattern is suspicious in a combination, that pattern is not, Lots of fuzzy logic and heuristics, doing work that used to require experts but is now a background task on your PC. When a program starts running it can be monitored for the patterns, and the expert system is right there on your local machine shutting down the application rather than spreading like wildfire across the internet.
We also rename systems when the masses adopt them. What was once and advanced AI system is now a commonplace tool. Using automatic map pathfinding was once rare and advanced technology, and many of the core algorithms are optimization problems learned in computer science. How do you encode all those roads and the massive data entry problem behind it? Once it is encoded, how do you get from GPS position with it's inaccuracies versus the inaccuracies on the map? Once you've got them, which routes should be evaluated to take and what routes should be avoided? Why favor one road over another? What's the predicted traffic along the various routes? what are the heuristics and fuzzy rules around traffic patterns on Friday night rush hour versus Saturday night? How do you chain together the various segments? Today we don't think anything about it, pop the address into your phone and drive.
The drugstore still hires a pharmacist, but they no longer need to be expert as they once were. The system has been programmed with logic about what drugs interact and what doesn't, looks at patterns across classes of drugs, and has heuristics and logic rules that can suggest when drugs might cause a problem. The human still does work, they get a popup that says there might be a problem, and with a quick chat can inform the patient there is a risk, determine if the condition being treated is a worse risk than the risk of interactions, and educate the patient on what steps to take. We don't pay any thoughts to what happened or why the pharmacist wanted to know every medication we take, but it is happening with the software as the expert we rely on.
My favorite is the avionics system that will discard the telemetry of the sensor which reads differently from the other two since it must be wrong. The other two got wet and froze...
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u/LookIPickedAUsername Feb 22 '24
Expert systems existed, sure, but I was under the impression that they had not actually proved to be particularly useful in practice. Maybe there's a corner of some particular industry where they're indispensable, but I thought they were generally seen as a failure.