Alexander Artikis

Alexander Artikis

Associate Professor of Artificial Intelligence

University of Piraeus

NCSR Demokritos

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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.


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Recent Projects

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.

REVEAL (2013-2016) was a FP7 project that discovered what is being said in social media, and determined how trustworthy that information is, based on predicting contributor impact and how much or to what extent this affects reputation or influence.

Selected Recent Publications

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


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


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


(2021). Online Learning Probabilistic Event Calculus Theories in Answer Set Programming. In Theory and Practice of Logic Programming Journal (TPLP).


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


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


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

PDF Code Slides Video

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

PDF Code Slides DOI

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


(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).


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


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


(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).


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


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


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



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

View Github repository