Online Learning Probabilistic Event Calculus Theories in Answer Set Programming
This work addresses the challenge of automating event pattern authoring in CER for domains like activity recognition and surveillance, offering an incremental improvement by integrating online learning with probabilistic reasoning in a logic-based framework.
The paper tackles the problem of learning probabilistic event patterns for Complex Event Recognition (CER) from streaming data, presenting an Answer Set Programming (ASP)-based system that learns weighted rules in the Event Calculus online, and demonstrates superior efficiency and predictive performance compared to state-of-the-art methods.
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 datasets 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).