  In the paper, we present the ADD-Lib, our efficient and easy to use framework
for Algebraic Decision Diagrams (ADDs). The focus of the ADD-Lib is not so much
on its efficient implementation of individual operations, which are taken by
other established ADD frameworks, but its ease and flexibility, which arise at
two levels: the level of individual ADD-tools, which come with a dedicated
user-friendly web-based graphical user interface, and at the meta level, where
such tools are specified. Both levels are described in the paper: the meta
level by explaining how we can construct an ADD-tool tailored for Random Forest
refinement and evaluation, and the accordingly generated Web-based
domain-specific tool, which we also provide as an artifact for cooperative
experimentation. In particular, the artifact allows readers to combine a given
Random Forest with their own ADDs regarded as expert knowledge and to
experience the corresponding effect.

Machine Learning;Artificial Intelligence;Programming Languages;Software Engineering