Complex Event Recognition (CER) systems detect event occurrences in streaming time-stamped input using predefined event patterns. Logic-based approaches are of special interest in CER, since, via Statistical Relational AI, they combine uncertainty-resilient reasoning with time and change, with machine learning, thus alleviating the cost of manual event pattern authoring. We present a system based on Answer Set Programming (ASP), capable of probabilistic reasoning with complex event patterns in the form of weighted rules in the Event Calculus, whose structure and weights are learnt online. We compare our ASP-based implementation with a Markov Logic-based one and with a number of state-of-the-art batch learning algorithms on CER data sets for activity recognition, maritime surveillance and fleet management. Our results demonstrate the superiority of our novel approach, both in terms of efficiency and predictive performance. This paper is under consideration for publication in Theory and Practice of Logic Programming (TPLP).

复杂事件识别 (CER) 系统使用预定义的事件模式检测流式时间戳输入中的事件发生。基于逻辑的方法对 CER 特别感兴趣，因为通过统计关系 AI，它们将不确定性弹性推理与时间和变化以及机器学习相结合，从而减轻了手动事件模式创作的成本。我们提出了一个基于答案集编程 (ASP) 的系统，能够在事件演算中以加权规则的形式对复杂事件模式进行概率推理，其结构和权重是在线学习的。我们将基于 ASP 的实现与基于马尔可夫逻辑的实现以及 CER 数据集上用于活动识别、海上监视和船队管理的许多最先进的批量学习算法进行比较。我们的结果证明了我们的新方法在效率和预测性能方面的优越性。这篇论文正在考虑发表在逻辑编程理论与实践 (TPLP) 上。