Abstract: 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 toward 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.