Editing Symbolic artificial intelligence (section)

==== Knowledge-based systems ====

As limitations with weak, domain-independent methods became more and more apparent,{{sfn|Russell|Norvig|2021|page=22}} researchers from all three traditions began to build [[knowledge representation|knowledge]] into AI applications.{{sfn|McCorduck|2004|pp=266–276, 298–300, 314, 421}}{{sfn|Russell|Norvig|2021|pp=22–23}} The knowledge revolution was driven by the realization that knowledge underlies high-performance, domain-specific AI applications.

[[Edward Feigenbaum]] said:
* "In the knowledge lies the power."<ref name="Feigenbaum">{{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 = 2022-07-14| date = June 2010| s2cid = 10239007| url = https://dl.acm.org/doi/10.1145/1743546.1743564| url-access = subscription}}</ref> 
to describe that high performance in a specific domain requires both general and highly domain-specific knowledge. Ed Feigenbaum and Doug Lenat called this The Knowledge Principle: 
{{Blockquote
|text=(1) The Knowledge Principle: if a program is to perform a complex task well, it must know a great deal about the world in which it operates.<br/>(2) A plausible extension of that principle, called the Breadth Hypothesis: there are two additional abilities necessary for intelligent behavior in unexpected situations: falling back on increasingly general knowledge, and analogizing to specific but far-flung knowledge.<ref name="Knowledge Principle">{{Cite journal| last1=Lenat| first1=Douglas B| last2=Feigenbaum| first2=Edward A| title=On the thresholds of knowledge| journal=Proceedings of the International Workshop on Artificial Intelligence for Industrial Applications| date=1988| pages=291–300| doi=10.1109/AIIA.1988.13308| s2cid=11778085}}</ref>}}