PDF | PostScript | doi:10.1613/jair.61

Given a knowledge base *KB* containing first-order and statistical facts, we consider a principled method, called the *random-worlds method*, for computing a degree of belief that some formula *Phi* holds given *KB*. If we are reasoning about a world or system consisting of *N* individuals, then we can consider all possible worlds, or first-order models, withdomain *{1,...,N}* that satisfy *KB*, and compute thefraction of them in which *Phi* is true. We define the degree of belief to be the asymptotic value of this fraction as *N* grows large. We show that when the vocabulary underlying *Phi* and*KB* uses constants and unary predicates only, we can naturally associate an *entropy* with each world. As *N* grows larger,there are many more worlds with higher entropy. Therefore, we can usea * maximum-entropy* computation to compute the degree of belief. This result is in a similar spirit to previous work in physics and artificial intelligence, but is far more general. Of equal interest to the result itself are the limitations on its scope. Most importantly, the restriction to unary predicates seems necessary. Although the random-worlds method makes sense in general, the connection to maximum entropy seems to disappear in the non-unary case. These observations suggest unexpected limitations to the applicability of maximum-entropy methods.