Ghent University Academic Bibliography

A shared temporal window of integration across cognitive control and reinforcement learning paradigms : a correlational study

Nicola Vasta, Shengjie Xu (UGent) , Tom Verguts (UGent) and Senne Braem (UGent)

(2025) MEMORY & COGNITION.

Journal Article
A1
open access

HephaestusForge : optimal microservice deployment across the Compute Continuum via Reinforcement Learning

José Pedro Pereira dos Santos (UGent) , Mattia Zaccarini, Filippo Poltronieri, Mauro Tortonesi, Cesare Stefanelli, Nicola Di Cicco and Filip De Turck (UGent)

(2025) FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE. 166.

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Journal Article
A1
open access

Gwydion : efficient auto-scaling for complex containerized applications in Kubernetes through Reinforcement Learning

José Pedro Pereira dos Santos (UGent) , Efstratios Reppas, Tim Wauters (UGent) , Bruno Volckaert (UGent) and Filip De Turck (UGent)

(2025) JOURNAL OF NETWORK AND COMPUTER APPLICATIONS. 234.

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Predicting and publishing accurate imbalance prices using Monte Carlo Tree Search

Fabio Pavirani (UGent) , Jonas Van Gompel (UGent) , Seyedsoroush Karimi Madahi (UGent) , Bert Claessens (UGent) and Chris Develder (UGent)

(2025) APPLIED ENERGY. 392.

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Journal Article
A2
open access

Radio resource management for intelligent neutral host (INH) in multi-operator environments

Hojjat Navidan (UGent) , Mostafa Naseri (UGent) , Ingrid Moerman (UGent) and Adnan Shahid (UGent)

(2024) IEEE OPEN JOURNAL OF THE COMMUNICATIONS SOCIETY. 5. p.1975-1986

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Journal Article
A1
open access

Compliant robust control for robotic insertion of soft bodies

Yi Liu (UGent) , Andreas Verleysen (UGent) and Francis wyffels (UGent)

(2024) IEEE ROBOTICS AND AUTOMATION LETTERS. 9(6). p.5711-5718

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Conference Paper
P1
open access

Efficient microservice deployment in Kubernetes multi-clusters through reinforcement learning

José Pedro Pereira dos Santos (UGent) , M. Zaccarini, F. Poltronieri, M. Tortonesi, C. Stefanelli, N. Di Cicco and Filip De Turck (UGent)

(2024) PROCEEDINGS OF 2024 IEEE/IFIP NETWORK OPERATIONS AND MANAGEMENT SYMPOSIUM, NOMS 2024. In IEEE IFIP Network Operations and Management Symposium

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Improving energy flexibility in photovoltaic-battery systems through switching reinforcement learning control

Siebe Paesschesoone (UGent) , Nezmin Kayedpour (UGent) , Carlo Manna and Guillaume Crevecoeur (UGent)

(2024) 2024 IEEE Conference on Technologies for Sustainability (SusTech). In IEEE Conference on Technologies for Sustainability SusTech p.57-62

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Learning temporal task specifications from demonstrations

Mattijs Baert (UGent) , Sam Leroux (UGent) and Pieter Simoens (UGent)

(2024) EXPLAINABLE AND TRANSPARENT AI AND MULTI-AGENT SYSTEMS, EXTRAAMAS 2024. In Lecture Notes in Computer Science 14847. p.81-98

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Conference Paper
P1
open access

Towards optimal load balancing in multi-zone Kubernetes clusters via reinforcement learning

José Pedro Pereira dos Santos (UGent) , Tim Wauters (UGent) , Filip De Turck (UGent) and Peter Steenkiste (UGent)

(2024) 2024 33RD INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATIONS AND NETWORKS, ICCCN 2024. In IEEE International Conference on Computer Communications and Networks

Academic Bibliography

A shared temporal window of integration across cognitive control and reinforcement learning paradigms : a correlational study

HephaestusForge : optimal microservice deployment across the Compute Continuum via Reinforcement Learning

Gwydion : efficient auto-scaling for complex containerized applications in Kubernetes through Reinforcement Learning

Predicting and publishing accurate imbalance prices using Monte Carlo Tree Search

Radio resource management for intelligent neutral host (INH) in multi-operator environments

Compliant robust control for robotic insertion of soft bodies

Efficient microservice deployment in Kubernetes multi-clusters through reinforcement learning

Improving energy flexibility in photovoltaic-battery systems through switching reinforcement learning control

Learning temporal task specifications from demonstrations

Towards optimal load balancing in multi-zone Kubernetes clusters via reinforcement learning

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