# Online Learning Probabilistic Event Calculus Theories in Answer Set Programming

arXiv: https://arxiv.org/abs/2104.00158 · HTML: https://mlsanity.com/paper/2104.00158/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 36 references that are in the map.
- First posted on arXiv 31 Mar 2021.

## Builds on (most cited first, 5 of 5)

- [Inductive Learning of Answer Set Programs from Noisy Examples](https://mlsanity.com/paper/1808.08441/) (2018)
- [Computing LPMLN Using ASP and MLN Solvers](https://mlsanity.com/paper/1707.06325/) (2017)
- [Incremental Learning of Event Definitions with Inductive Logic Programming](https://mlsanity.com/paper/1402.5988/) (2014)
- [Online Event Recognition from Moving Vehicles: Application Paper](https://mlsanity.com/paper/1907.11007/) (2019)
- [Online Event Recognition from Moving Vessel Trajectories](https://mlsanity.com/paper/1601.06041/) (2016)

## Built on by (most cited first, 4 of 4)

- [Inductive Learning of Robot Task Knowledge from Raw Data and Online Expert Feedback](https://mlsanity.com/paper/2501.07507/) (2025)
- [Movement Analytics: Current Status, Application to Manufacturing, and Future Prospects from an AI Perspective](https://mlsanity.com/paper/2210.01344/) (2022)
- [A Translation of Probabilistic Event Calculus into Markov Decision Processes](https://mlsanity.com/paper/2507.12989/) (2025)
- [XLOG: A CUDA-Native Engine for Neurosymbolic Integration](https://mlsanity.com/paper/2609.27203/) (2026)

## Abstract (arXiv)

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).