Bayesian networks
Pearl proposed representing dependencies between events as a graph, and showed how to propagate new evidence through it locally, without recomputing everything.
Why it matters
Uncertainty stopped being a heuristic add-on: there was now a way of computing probabilities that scales.
A full joint distribution grows exponentially with the number of variables, which is why early systems made do with unjustified coefficients. The graph encodes conditional independence and makes the computation feasible. This returned probability to AI after two decades of symbolic dominance and led to modern probabilistic models. Pearl received the Turing Award in 2011.