论文标题
FANOOS：多分辨率，多强度的交互式解释
Fanoos: Multi-Resolution, Multi-Strength, Interactive Explanations for Learned Systems
论文作者
论文摘要
在复杂的环境中，机器学习对于控制安全性和财务关键组成部分的行为变得越来越重要，在复杂的环境中，无法理解一般的学术组件，尤其是神经网络，对其采用构成了严重的障碍。学习系统的可解释性和可解释性方法吸引了学术关注，但是当前方法的重点仅在解释的一个方面，固定的抽象水平，如果有任何正式保证，则可以阻止这些解释被相关的利益相关者（例如，最终用户，认证机构，工程师，工程师，工程师）具有多样化的背景和各种情况。我们介绍了Fanoos，这是一个结合正式验证技术，启发式搜索和用户互动的框架，以探索所需的粒度和忠诚度的解释。我们证明了Fanoos能够根据对倒置双摆的学习控制器和学识渊博的CPU使用模型的用户要求产生和调整解释的抽象性。
Machine learning is becoming increasingly important to control the behavior of safety and financially critical components in sophisticated environments, where the inability to understand learned components in general, and neural nets in particular, poses serious obstacles to their adoption. Explainability and interpretability methods for learned systems have gained considerable academic attention, but the focus of current approaches on only one aspect of explanation, at a fixed level of abstraction, and limited if any formal guarantees, prevents those explanations from being digestible by the relevant stakeholders (e.g., end users, certification authorities, engineers) with their diverse backgrounds and situation-specific needs. We introduce Fanoos, a framework for combining formal verification techniques, heuristic search, and user interaction to explore explanations at the desired level of granularity and fidelity. We demonstrate the ability of Fanoos to produce and adjust the abstractness of explanations in response to user requests on a learned controller for an inverted double pendulum and on a learned CPU usage model.
