Editing Symbolic artificial intelligence (section)

==== Success with expert systems ====
{{Main|Expert systems}}

This "knowledge revolution" led to the development and deployment of [[expert system]]s (introduced by [[Edward Feigenbaum]]), the first commercially successful form of AI software.{{sfn|Russell|Norvig|2021|pp=22–24}}{{sfn|McCorduck|2004|pp=327–335, 434–435}}{{sfn|Crevier|1993|pp=145–62, 197–203}}

Key expert systems were:

* [[DENDRAL]], which found the structure of organic molecules from their chemical formula and mass spectrometer readings.
* [[MYCIN]], which diagnosed bacteremia – and suggested further lab tests, when necessary – by interpreting lab results, patient history, and doctor observations. "With about 450 rules, MYCIN was able to perform as well as some experts, and considerably better than junior doctors."{{sfn|Russell|Norvig|2021|p=23}}
* [[Internist-I|INTERNIST]] and [[CADUCEUS (expert system)|CADUCEUS]] which tackled internal medicine diagnosis. Internist attempted to capture the expertise of the chairman of internal medicine at the [[University of Pittsburgh School of Medicine]] while CADUCEUS could eventually diagnose up to 1000 different diseases.
* GUIDON, which showed how a knowledge base built for expert problem solving could be repurposed for teaching.{{sfn|Clancey|1987}}
* [[XCON]], to configure VAX computers, a then laborious process that could take up to 90 days. XCON reduced the time to about 90 minutes.{{sfn|Kautz|2022|p=110}}

[[DENDRAL]] is considered the first expert system that relied on knowledge-intensive problem-solving. It is described below, by [[Ed Feigenbaum]], from a [[Communications of the ACM]] interview, [https://cacm.acm.org/magazines/2010/6/92472-an-interview-with-ed-feigenbaum/fulltext|An Interview with Ed Feigenbaum]:

{{Blockquote
|text=One of the people at Stanford interested in computer-based models of mind was [[Joshua Lederberg]], the 1958 Nobel Prize winner in genetics. When I told him I wanted an induction "sandbox", he said, "I have just the one for you." His lab was doing mass spectrometry of amino acids. The question was: how do you go from looking at the spectrum of an amino acid to the chemical structure of the amino acid? That's how we started the DENDRAL Project: I was good at heuristic search methods, and he had an algorithm that was good at generating the chemical problem space.

We did not have a grandiose vision. We worked bottom up. Our chemist was [[Carl Djerassi]], inventor of the chemical behind the birth control pill, and also one of the world's most respected mass spectrometrists. Carl and his postdocs were world-class experts in mass spectrometry. We began to add to their knowledge, inventing knowledge of engineering as we went along. These experiments amounted to titrating DENDRAL more and more knowledge. The more you did that, the smarter the program became. We had very good results.

The generalization was: in the knowledge lies the power. That was the big idea. In my career that is the huge, "Ah ha!," and it wasn't the way AI was being done previously. Sounds simple, but it's probably AI's most powerful generalization.<ref name="Feignebaum Interview">{{Cite journal| doi = 10.1145/1743546.1743564| issn = 0001-0782| volume = 53| issue = 6| pages = 41–45| last = Shustek| first = Len| title = An interview with Ed Feigenbaum| journal = Communications of the ACM| accessdate = 2022-08-05| date = 2010| s2cid = 10239007| url = https://cacm.acm.org/magazines/2010/6/92472-an-interview-with-ed-feigenbaum/fulltext| url-access = subscription}}</ref>}}

The other expert systems mentioned above came after DENDRAL. MYCIN exemplifies the classic expert system architecture of a knowledge-base of rules coupled to a symbolic reasoning mechanism, including the use of certainty factors to handle uncertainty. GUIDON shows how an explicit knowledge base can be repurposed for a second application, tutoring, and is an example of an [[intelligent tutoring system]], a particular kind of knowledge-based application. Clancey showed that it was not sufficient simply to use [[MYCIN]]'s rules for instruction, but that he also needed to add rules for dialogue management and student modeling.{{sfn|Clancey|1987}} XCON is significant because of the millions of dollars it saved [[Digital Equipment Corporation|DEC]], which triggered the expert system boom where most all major corporations in the US had expert systems groups, to capture corporate expertise, preserve it, and automate it:

{{Blockquote
|text=By 1988, DEC's AI group had 40 expert systems deployed, with more on the way. DuPont had 100 in use and 500 in development. Nearly every major U.S. corporation had its own Al group and was either using or investigating expert systems.{{sfn|Russell|Norvig|2021|p=23}}
}}

Chess expert knowledge was encoded in [[Deep Blue (chess computer)|Deep Blue]]. In 1996, this allowed [[IBM]]'s [[Deep Blue (chess computer)|Deep Blue]], with the help of symbolic AI, to win in a game of chess against the world champion at that time, [[Garry Kasparov]].<ref>{{Cite web|title=The fascination with AI: what is artificial intelligence?|url=https://www.ionos.com/digitalguide/online-marketing/online-sales/what-is-artificial-intelligence/|access-date=2021-12-02|website=IONOS Digitalguide|language=en}}</ref>

===== Architecture of knowledge-based and expert systems =====

A key component of the system architecture for all expert systems is the knowledge base, which stores facts and rules for problem-solving.{{sfn|Hayes-Roth|Murray|Adelman|2015}}
The simplest approach for an expert system knowledge base is simply a collection or network of [[Production system (computer science)|production rules]]. Production rules connect symbols in a relationship similar to an If-Then statement. The expert system processes the rules to make deductions and to determine what additional information it needs, i.e. what questions to ask, using human-readable symbols. For example, [[OPS5]], [[CLIPS]] and their successors [[Jess (programming language)|Jess]] and [[Drools]] operate in this fashion.

Expert systems can operate in either a [[forward chaining]] – from evidence to conclusions – or [[backward chaining]] – from goals to needed data and prerequisites – manner. More advanced knowledge-based systems, such as [[Soar (cognitive architecture)|Soar]] can also perform meta-level reasoning, that is reasoning about their own reasoning in terms of deciding how to solve problems and monitoring the success of problem-solving strategies.

[[Blackboard system]]s are a second kind of [[knowledge-based system|knowledge-based]] or [[expert system]] architecture. They model a community of experts incrementally contributing, where they can, to solve a problem. The problem is represented in multiple levels of abstraction or alternate views. The experts (knowledge sources) volunteer their services whenever they recognize they can contribute. Potential problem-solving actions are represented on an agenda that is updated as the problem situation changes. A controller decides how useful each contribution is, and who should make the next problem-solving action. One example, the BB1 blackboard architecture<ref name="BB1">{{Cite journal| doi = 10.1016/0004-3702(85)90063-3| volume = 26| issue = 3| pages = 251–321| last = Hayes-Roth| first = Barbara| title = A blackboard architecture for control| journal = Artificial Intelligence| date = 1985}}</ref> was originally inspired by studies of how humans plan to perform multiple tasks in a trip.<ref name="OPM">{{Cite conference| publisher = RAND| last = Hayes-Roth| first = Barbara| title = Human Planning Processes| date = 1980}}</ref> An innovation of BB1 was to apply the same blackboard model to solving its control problem, i.e., its controller performed meta-level reasoning with knowledge sources that monitored how well a plan or the problem-solving was proceeding and could switch from one strategy to another as conditions – such as goals or times – changed. BB1 has been applied in multiple domains: construction site planning, intelligent tutoring systems, and real-time patient monitoring.