The Book of Why: The New Science of Cause and Effect

by Judea Pearl

The late 1970s, then, were a time of ferment in the AI community over the question of how to deal with uncertainty. There was no shortage of ideas. Lotfi Zadeh of Berkeley offered “fuzzy logic,” in which statements are neither true nor false but instead take a range of possible truth values. Glen Shafer of the University of Kansas proposed “belief functions,” which assign two probabilities to each fact, one indicating how likely it is to be “possible,” the other, how likely it is to be “provable.” Edward Feigenbaum and his colleagues at Stanford University tried “certainty factors,” which inserted numerical measures of uncertainty into their deterministic rules for inference. Unfortunately, although ingenious, these approaches suffered a common flaw: they modeled the expert, not the world, and therefore tended to produce unintended results.