Machine learning becomes increasingly important to tune or even synthesize the behavior of safety-critical components in highly non-trivial 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 flexible 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.

中文翻译：

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Fanoos：学习系统的多分辨率、多强度、交互式解释

机器学习对于在高度非平凡的环境中调整甚至综合安全关键组件的行为变得越来越重要，在这些环境中，无法理解一般学习组件，尤其是神经网络，对它们的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已经引起了学术界的广泛关注，但是当前的方法只关注一个方面的解释，处于固定的抽象级别，并且如果有任何正式保证的话，这些方法也很有限，这使得相关利益相关者无法消化这些解释（例如，最终用户、认证机构、工程师）具有不同的背景和特定情况的需求。我们介绍了 Fanoos，这是一个灵活的框架，用于结合形式验证技术、启发式搜索、和用户交互以在所需的粒度和保真度级别上探索解释。我们展示了 Fanoos 能够根据用户对倒双摆学习控制器和学习 CPU 使用模型的请求，生成和调整解释的抽象性。