Anomaly Detection and Classification to enable Self-Explainability of Autonomous Systems
Anomaly Detection and Classification to enable Self-Explainability of Autonomous Systems
复制标题
异常检测和分类以实现自治系统的自我解释
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
S. Glesner
中科院分区：
文献类型：
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作者：
Florian Ziesche;V. Klös;S. Glesner
虽然自主系统在我们的日常生活和行业中的重要性越来越大，但我们必须确保这种发展被用户接受。人类和自主系统之间成功合作的一个关键因素是基本的理解，允许用户预测系统的行为。由于其复杂性，完全理解既不可能，也不可取。相反，我们提出自我解释作为一种解决方案。一个自我解释的系统自主地解释与预期行为不同的行为。作为实现这一愿景的第一步，我们提出了一种方法，用于检测异常行为，需要一个解释，并通过将其分类到具有类似原因的类中，以减少这种行为的可能原因的巨大搜索空间。我们设想我们的方法是可以添加到任何自治系统的解释组件的一部分。
While the importance of autonomous systems in our daily lives and in the industry increases, we have to ensure that this development is accepted by their users. A crucial factor for a successful cooperation between humans and autonomous systems is a basic understanding that allows users to anticipate the behavior of the systems. Due to their complexity, complete understanding is neither achievable, nor desirable. Instead, we propose self-explainability as a solution. A self-explainable system autonomously explains behavior that differs from anticipated behavior. As a first step towards this vision, we present an approach for detecting anomalous behavior that requires an explanation and for reducing the huge search space of possible reasons for this behavior by classifying it into classes with similar reasons. We envision our approach to be part of an explanation component that can be added to any autonomous system.