Rules All the Way Down
From the 1950s to the 1980s, “AI” meant manipulating symbols. Represent the world as facts and rules, then reason over them. It is called symbolic AI, or GOFAI — “good old-fashioned AI.”
The machinery
Three ideas carried the era. Knowledge representation: encode facts as symbols
(IF fever AND rash THEN ...). Search: explore a tree of possible states toward a
goal — chess grandmasters fell to deep search. Expert systems: bottle a specialist’s
rules and sell it. For narrow, rule-like domains, these worked, and sometimes made real
money.
The brittleness
Symbols are precise, and the world is not. A rule-base has no idea what to do with a typo, a blurred photograph, or a sentence it has never seen. Every exception you handle reveals three more. The knowledge has to be written by a human, by hand, forever.
What it got right
Do not dismiss this era. Symbolic AI gave us search algorithms, theorem provers, databases, logic programming, and the idea that knowledge can be explicit and inspectable. Modern agent systems still use planning and explicit knowledge — they just pair it with a model that can handle the messy parts. The lesson is not “symbols were wrong.” It is “symbols alone are not enough.”