ADD-Lib: Decision Diagrams in Practice
This provides a user-friendly tool for practitioners working with decision diagrams, though it is incremental as it builds on existing ADD frameworks.
The paper presents ADD-Lib, a framework for Algebraic Decision Diagrams that emphasizes ease of use and flexibility through a web-based graphical interface and meta-level tool specification, demonstrated with a Random Forest refinement tool.
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.