Alexander Artikis

Alexander Artikis

Associate Professor of Artificial Intelligence

University of Piraeus

NCSR Demokritos

$ whoami

I am an Associate Professor at the University of Piraeus, and a Research Associate at the National Centre for Scientific Research (NCSR) "Demokritos", leading the Complex Event Recognition group. My research interests lie in the fields of artificial intelligence and distributed systems. Most of my work has been on complex event recognition and (norm-governed) multi-agent systems.

For details about my research, please visit the web page of the Complex Event Recognition group.

Book

Guide to Maritime Informatics

The book presents the main technological innovations which are having a disruptive effect on the maritime industry. We present the required knowledge, algorithmic approaches and technical details, before showcasing real-world applications.

Get the book See more

Recent Projects

ENEXA is an H2020 HUMAN project aiming at developing scalable, transparent and explainable machine learning algorithms for knowledge graphs, while maintaining their completeness and correctness.

EVENFLOW is an H2020 HUMAN project aiming at developing hybrid learning techniques for complex event forecasting, which combine deep learning with logic-based learning and reasoning into neuro-symbolic forecasting models.

CREXDATA is an H2020 DATA project whose vision is to develop a generic platform for real-time critical situation management, including flexible action planning and agile decision making over streaming data of extreme scale and complexity, using federated predictive analytics and forecasting under uncertainty algorithms.

VesselAI (2021-2024) aims at realising a holistic, AI-empowered framework for simulating and predicting vessel behaviour and manoeuvring, ship energy design optimisation, autonomous shipping and fleet intelligence.

ARIADNE (2019-2022) is a H2020 project bringing together a novel high-frequency radio architecture, and an Artificial Intelligence network processing and management approach, in order to allow for continuous reliable High Bandwidth connections in the Beyond 5G era.

INFORE (2019-2021) is a H2020 project that aims to address the challenges posed by huge datasets and pave the way for real-time, interactive, extreme-scale analytics and forecasting.

Track & Know (2018-2020) is a H2020 project with a mission to research, develop and exploit a new software framework for increasing the efficiency of Big Data applications in the transport, mobility, motor insurance and health sectors.

datACRON (2016-2019) was a H2020 project that introduced novel methods for detecting threats and abnormal activity in very large numbers of moving entities, operating in large geographic areas.

SPEEDD (2014-2017) was a FP7 European project that developed a prototype for proactive event-driven decision-making: decisions were triggered by forecasting events-whether they correspond to problems or opportunities-instead of reacting to them once they happen.

Selected Recent Publications

Click here for the complete list

(2024). Online semi-supervised learning of composite event rules by combining structure and mass-based predicate similarity. In Machine Learning, 113(3), 1445–1481.

PDF DOI

(2023). Online Event Recognition over Noisy Data Streams. In International Journal of Approximate Reasoning (IJAR), 161:108993.

PDF Code DOI

(2023). Online Learning Probabilistic Event Calculus Theories in Answer Set Programming. In Theory and Practice of Logic Programming Journal (TPLP), 23(2), pp. 362–386.

PDF DOI

(2023). Complex Event Recognition with Allen Relations. In Proceedings of the 20th International Conference on Principles of Knowledge Representation and Reasoning (KR), 502–511.

PDF Code Slides DOI

(2022). Incremental Event Calculus for Run-Time Reasoning. In Journal of Artificial Intelligence Research (JAIR), 73, pp. 967–1023.

PDF Code DOI

(2022). Complex Event Forecasting with Prediction Suffix Trees. In International Journal on Very Large DataBases (VLDBJ).

PDF DOI

(2022). Stream Reasoning with Cycles. In Proceedings of the 19th International Conference on Principles of Knowledge Representation and Reasoning (KR).

PDF Code Slides DOI

(2021). A Probabilistic Interval-based Event Calculus for Activity Recognition. In Annals of Mathematics and Artificial Intelligence Journal (AMAI), 89.

PDF DOI

(2020). Complex Event Recognition in the Big Data Era: A Survey. In The International Journal on Very Large DataBases (VLDBJ), 29(1).

PDF DOI

(2020). Online Probabilistic Interval-based Event Calculus. In European Conference on Artificial Intelligence (ECAI).

PDF Code Slides Video DOI

(2020). Fine-Tuned Compressed Representations of Vessel Trajectories.. In International Conference on Information and Knowledge Management (CIKM).

PDF Code Slides DOI

(2020). WOLED: A Tool for Online Learning Weighted Answer Set Rules for Temporal Reasoning Under Uncertainty. In Proceedings of the 17th International Conference on Principles of Knowledge Representation and Reasoning (KR).

PDF Code Slides Video DOI

(2019). Semi-Supervised Online Structure Learning for Composite Event Recognition. In Machine Learning, 108(7).

PDF DOI

(2018). Online Learning of Weighted Relational Rules for Complex Event Recognition. In European Conference on Machine Learning (ECML-PKDD).

PDF Slides DOI

(2017). Probabilistic Complex Event Recognition: A Survey. In ACM Computing Surveys, 50(5).

PDF DOI

(2017). Online event recognition from moving vessel trajectories. In GeoInformatica, 21(2).

PDF DOI

(2016). Online Learning of Event Definitions. In Theory and Practice of Logic Programming (TPLP), 16(5-6).

PDF Code DOI

(2016). OSLα: Online Structure Learning using Background Knowledge Axiomatization. In European Conference on Machine Learning (ECML-PKDD).

PDF Slides DOI

(2015). Incremental Learning of Event Definitions with Inductive Logic Programming. In Machine Learning, 100(2-3).

PDF Code DOI

(2015). An Event Calculus for Event Recognition. In IEEE Transactions on Knowledge and Data Engineering (TKDE), 27(4).

PDF Code DOI

(2015). A probabilistic logic programming event calculus. In Theory and Practice of Logic Programming (TPLP), 15(2).

PDF Code DOI

(2015). Probabilistic Event Calculus for Event Recognition. In ACM Transactions on Computational Logic (TOCL), 16(2).

PDF DOI

Software

RTEC is an open-source Event Calculus implementation optimised for stream reasoning.

View Github repository