ADD-Lib：決策實踐圖（CS AI）

• 2020 年 1 月 2 日
• 筆記

在本文中，我們介紹了ADD-Lib，這是我們高效且易於使用的代數決策圖（ADD）框架。ADD-Lib的重點不是其有效地實現其他已建立的ADD框架所採用的單個操作，而是其易用性和靈活性，它體現在兩個層次上：各個ADD工具的層次，具有專用的用戶友好型基於Web的圖形用戶界面，並在元級別指定了此類工具。在本文中描述了兩個級別：通過解釋我們如何構建針對隨機森林優化和評估量身定製的ADD工具的元級別，以及相應生成的基於Web的領域特定工具，我們還將其作為協作的工件提供實驗。特別是，工件允許讀者結合一個給定的隨機森林與他們自己的添加作為專家知識，並體驗相應的效果。

原文題目：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