ADD-Lib: Decision Diagrams in Practice

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 webbased 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.

Verifying Belief-Based Programs via Symbolic Dynamic Programming

“…For this, we exploit the idea of firstorder algebraic decision diagram (FOADD) in [35]. The algebraic decision diagram (ADD) [2,18] is a data structure that compactly represents a Boolean function {0, 1} n → R using a directed acyclic graph. Nevertheless, the propositional nature prevents us from using ADDs directly since case statements contain general FOL formulas (potentially with modality [a] and B) and program expressions.…”