  Answer set programming (ASP) is a popular declarative programming paradigm
with a wide range of applications in artificial intelligence. Oftentimes, when
modeling an AI problem with ASP, and in particular when we are interested
beyond simple search for optimal solutions, an actual solution, differences
between solutions, or number of solutions of the ASP program matter. For
example, when a user aims to identify a specific answer set according to her
needs, or requires the total number of diverging solutions to comprehend
probabilistic applications such as reasoning in medical domains. Then, there
are only certain problem specific and handcrafted encoding techniques available
to navigate the solution space of ASP programs, which is oftentimes not enough.
In this paper, we propose a formal and general framework for interactive
navigation towards desired subsets of answer sets analogous to faceted
browsing. Our approach enables the user to explore the solution space by
consciously zooming in or out of sub-spaces of solutions at a certain
configurable pace. We illustrate that weighted faceted navigation is
computationally hard. Finally, we provide an implementation of our approach
that demonstrates the feasibility of our framework for incomprehensible
solution spaces.
