  We present a formulation of the problem of probabilistic model checking as
one of query evaluation over probabilistic logic programs. To the best of our
knowledge, our formulation is the first of its kind, and it covers a rich class
of probabilistic models and probabilistic temporal logics. The inference
algorithms of existing probabilistic logic-programming systems are well defined
only for queries with a finite number of explanations. This restriction
prohibits the encoding of probabilistic model checkers, where explanations
correspond to executions of the system being model checked. To overcome this
restriction, we propose a more general inference algorithm that uses finite
generative structures (similar to automata) to represent families of
explanations. The inference algorithm computes the probability of a possibly
infinite set of explanations directly from the finite generative structure. We
have implemented our inference algorithm in XSB Prolog, and use this
implementation to encode probabilistic model checkers for a variety of temporal
logics, including PCTL and GPL (which subsumes PCTL*). Our experiment results
show that, despite the highly declarative nature of their encodings, the model
checkers constructed in this manner are competitive with their native
implementations.
