ADD-Lib：决策实践图（CS AI）

• 2020 年 1 月 2 日
• 筆記

在本文中，我们介绍了ADD-Lib，这是我们高效且易于使用的代数决策图（ADD）框架。ADD-Lib的重点不是其有效地实现其他已建立的ADD框架所采用的单个操作，而是其易用性和灵活性，它体现在两个层次上：各个ADD工具的层次，具有专用的用户友好型基于Web的图形用户界面，并在元级别指定了此类工具。在本文中描述了两个级别：通过解释我们如何构建针对随机森林优化和评估量身定制的ADD工具的元级别，以及相应生成的基于Web的领域特定工具，我们还将其作为协作的工件提供实验。特别是，工件允许读者结合一个给定的随机森林与他们自己的添加作为专家知识，并体验相应的效果。

原文题目：ADD-Lib: Decision Diagrams in Practice

原文：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.

原文作者：Frederik Gossen,Alnis Murtovi,Philip Zweihoff,Bernhard Steffen

原文地址：https://arxiv.org/abs/1912.11308