  The Credal semantics is a probabilistic extension of the answer set semantics
which can be applied to programs that may or may not be stratified. It assigns
to atoms a set of acceptable probability distributions characterised by its
lower and upper bounds. Performing exact probabilistic inference in the Credal
semantics is computationally intractable. This paper presents a first solver,
based on sampling, for probabilistic inference under the Credal semantics
called PASOCS (Parallel Approximate SOlver for the Credal Semantics). PASOCS
performs both exact and approximate inference for queries given evidence.
Approximate solutions can be generated using any of the following sampling
methods: naive sampling, Metropolis-Hastings and Gibbs Markov Chain
Monte-Carlo. We evaluate the fidelity and performance of our system when
applied to both stratified and non-stratified programs. We perform a sanity
check by comparing PASOCS to available systems for stratified programs, where
the semantics agree, and show that our system is competitive on unstratified
programs.
