Probability theory assigns values between zero and one to uncertain events and defines rules for combining those values consistently. It provides concepts such as random variables, probability distributions, conditional probability, expectation, and dependence, which let a system reason about more than one possible outcome.
In artificial intelligence, probability can express uncertainty in data, model predictions, hidden states, and future consequences. A numerical probability is useful only when its assumptions and calibration are appropriate, so systems should test whether stated confidence corresponds to observed outcomes rather than treating every score as trustworthy.
