# ADD-Lib: Decision Diagrams in Practice

arXiv: https://arxiv.org/abs/1912.11308 · HTML: https://mlsanity.com/paper/1912.11308/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Dec 2019.

## Built on by (most cited first, 1 of 1)

- [A survey and taxonomy of methods interpreting random forest models](https://mlsanity.com/paper/2407.12759/) (2024)

## Abstract (arXiv)

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.