English

ADD-Lib: Decision Diagrams in Practice

Machine Learning 2020-02-18 v1 Artificial Intelligence Programming Languages Software Engineering

Abstract

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.

Keywords

Cite

@article{arxiv.1912.11308,
  title  = {ADD-Lib: Decision Diagrams in Practice},
  author = {Frederik Gossen and Alnis Murtovi and Philip Zweihoff and Bernhard Steffen},
  journal= {arXiv preprint arXiv:1912.11308},
  year   = {2020}
}
R2 v1 2026-06-23T12:55:37.147Z