Many autonomous systems, such as robots and self-driving cars, involve real-time decision making in complex environments, and require prediction of future outcomes from limited data. Moreover, their decisions are increasingly required to be interpretable to humans for safe and trustworthy co-existence. This paper is a first step towards interpretable learning-based robot control. We introduce a novel learning problem, called incremental formula and predictor learning, to generate binary classifiers with temporal logic structure from time-series data. The classifiers are represented as pairs of Signal Temporal Logic (STL) formulae and predictors for their satisfaction. The incremental property provides prediction of labels for prefix signals that are revealed over time. We propose a boosted decision-tree algorithm that leverages weak, but computationally inexpensive, learners to increase prediction and runtime performance. The effectiveness and classification accuracy of our algorithms are evaluated on autonomous-driving and naval surveillance case studies.

中文翻译：

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使用增强决策树从数据推断时间逻辑属性

许多自动系统，例如机器人和自动驾驶汽车，都需要在复杂的环境中进行实时决策，并且需要根据有限的数据预测未来的结果。此外，为了安全和可信赖的共存，越来越多地要求他们的决定对人类是可解释的。本文是迈向可解释的基于学习的机器人控制的第一步。我们引入了一个新的学习问题，称为增量公式和预测变量学习，以从时间序列数据中生成具有时间逻辑结构的二进制分类器。分类器表示为成对的信号时态逻辑（STL）公式和对它们的满意度的预测因子。增量属性为随着时间的流逝显示的前缀信号提供标签的预测。我们提出了一种增强的决策树算法，该算法利用了弱，但计算量不大，学习者可以提高预测和运行时性能。我们的算法的有效性和分类准确性在自动驾驶和海军监视案例研究中进行了评估。