Abstract
While probabilistic programming is a powerful
tool, uncertainty is not always of a probabilistic
kind. Some types of uncertainty are better captured
using ranking theory, which is an alternative to
probability theory where uncertainty is measured
using degrees of surprise on the integer scale from
0 to ?. In this paper we combine probabilistic
programming methodology with ranking theory
and develop a ranked programming language. We
use the Scheme programming language a basis and
extend it with the ability to express both normal
and exceptional behaviour of a model, and perform
inference on such models. Like probabilistic
programming, our approach provides a simple and
flexible way to represent and reason with models
involving uncertainty, but using a coarser grained
and computationally simpler kind of uncertainty