Epistemic AI

Project Information

E-pi

Grant agreement ID: 964505

DOI

10.3030/964505

Project closed

EC signature date 17 December 2020

Start date 1 March 2021

End date 28 February 2026

Funded under

• EXCELLENT SCIENCE - Future and Emerging Technologies (FET)

Total cost

€ 3 016 618,75

EU contribution

€ 3 016 618,75

Coordinated by

OXFORD BROOKES UNIVERSITY
United Kingdom

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Deliverables

Documents, reports (6)

Report on epistemic reinforcement learning, with codebase
Report on pointwise optimisation under epistemic uncertainty
Report on methods from uncertainty theory
Report on epistemic learning theory
Report on use case definition
Report on set-based learning

Websites, patent fillings, videos etc. (2)

Thematic workshop no 1
Website and project logo

Project website and logo are finalised

Other (1)

Report on epistemic unsupervised learning, with code

Publications

Peer reviewed articles (11)

DeepSmoke: Deep learning model for smoke detection and segmentation in outdoor environments

Author(s): Salman Khan; Salman Khan; Khan Muhammad; Tanveer Hussain; Javier Del Ser; Fabio Cuzzolin; Siddhartha Bhattacharyya; Zahid Akhtar; Victor Hugo C. de Albuquerque
Published in: Expert Systems with Applications, 2021, Page(s) 115-125, ISSN 0957-4174
Publisher: Pergamon Press Ltd.
DOI: 10.1016/j.eswa.2021.115125

ROAD-R: the autonomous driving dataset with logical requirements

Author(s): Eleonora Giunchiglia, Mihaela Cătălina Stoian, Salman Khan, Fabio Cuzzolin, Thomas Lukasiewicz
Published in: Machine Learning, Issue 112, 2023, Page(s) 3261-3291, ISSN 0885-6125
Publisher: Kluwer Academic Publishers
DOI: 10.1007/s10994-023-06322-z

Fleet planning under demand and fuel price uncertainty using actor–critic reinforcement learning

Author(s): Izaak L. Geursen, Bruno F. Santos, Neil Yorke-Smith
Published in: Journal of Air Transport Management, Issue 109, 2023, Page(s) 102397, ISSN 0969-6997
Publisher: Pergamon Press Ltd.
DOI: 10.1016/j.jairtraman.2023.102397

Most likely heteroscedastic Gaussian process via kernel smoothing

Author(s): Ghifari Adam Faza, Nasrulloh R.B.S. Loka, Keivan Shariatmadar, Hans Hallez, David Moens
Published in: Knowledge-Based Systems, Issue 328, 2026, Page(s) 114202, ISSN 0950-7051
Publisher: Elsevier BV
DOI: 10.1016/j.knosys.2025.114202

Uncertainty measures: A critical survey

Author(s): Fabio Cuzzolin
Published in: Information Fusion, Issue 114, 2024, Page(s) 102609, ISSN 1566-2535
Publisher: Elsevier BV
DOI: 10.1016/j.inffus.2024.102609

Berth planning and real-time disruption recovery: a simulation study for a tidal port

Author(s): Jaap-Jan van der Steeg, Menno Oudshoorn, Neil Yorke-Smith
Published in: Flexible Services and Manufacturing Journal, Issue 35, 2023, Page(s) 70-110, ISSN 1936-6582
Publisher: Springer Pub. Co.,
DOI: 10.1007/s10696-022-09473-8

Enhancing the dependability of autonomous surface vehicles through robustness benchmarking of real-time object detection models

Author(s): Yunjia Wang, Zihao Zhang, Kaizheng Wang, Holger Caesar, Jeroen Boydens, Davy Pissoort, Mathias Verbeke
Published in: Expert Systems with Applications, Issue 296, 2026, Page(s) 129151, ISSN 0957-4174
Publisher: Pergamon Press Ltd.
DOI: 10.1016/j.eswa.2025.129151

Theory of Mind and Preference Learning at the Interface of Cognitive Science, Neuroscience, and AI: A Review

Author(s): Christelle Langley, Bogdan-Ionut Cirstea, Fabio Cuzzolin and Barbara J. Sahakian
Published in: Frontiers in Artificial Intelligence, Issue April 5 2022, 2022, ISSN 2624-8212
Publisher: Frontiers Media S.A.
DOI: 10.3389/frai.2022.778852

ROAD: The ROad event Awareness Dataset for autonomous Driving

Author(s): Gurkirt Singh, Stephen Akrigg, Manuele Di Maio, Valentina Fontana, Reza Javanmard Alitappeh, Suman Saha, Kossar Jeddisaravi, Farzad Yousefi, Jacob Culley, Tom Nicholson, Jordan Omokeowa, Salman Khan, Stanislao Grazioso, Andrew Bradley, Giuseppe Di Gironimo, Fabio Cuzzolin
Published in: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, ISSN 1939-3539
Publisher: IEEE
DOI: 10.1109/tpami.2022.3150906

A Review of Uncertainty Representation and Quantification in Neural Networks

Author(s): Kaizheng Wang, Fabio Cuzzolin, Keivan Shariatmadar, David Moens, Hans Hallez
Published in: IEEE Transactions on Pattern Analysis and Machine Intelligence, Issue 48, 2026, Page(s) 2476-2495, ISSN 0162-8828
Publisher: Institute of Electrical and Electronics Engineers
DOI: 10.1109/tpami.2025.3626645

CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks

Author(s): Kaizheng Wang, Keivan Shariatmadar, Shireen Kudukkil Manchingal, Fabio Cuzzolin, David Moens, Hans Hallez
Published in: Neural Networks, Issue 185, 2025, Page(s) 107198, ISSN 0893-6080
Publisher: Pergamon Press Ltd.
DOI: 10.1016/j.neunet.2025.107198

Other (19)

Generalising realisability in statistical learning theory under epistemic uncertainty

Author(s): F. Cuzzolin
Published in: arXiv preprint arXiv:2402.14759, Issue 22 Feb 2024, 2024
Publisher: Cornell University
DOI: 10.48550/arxiv.2402.14759

Set-based v.s. Distribution-based Representations of Epistemic Uncertainty: A Comparative Study

Author(s): K. Wang, Y. Wang, F. Cuzzolin, D. Moens, H. Hallez, S.L. Chau
Published in: arXiv preprint arXiv:2602.22747, Issue February 26 2026, 2026
Publisher: Cornell University
DOI: 10.48550/arxiv.2602.22747

Random-Set Large Language Model

Author(s): M. Mubashar, S.K. Manchingal, F. Cuzzolin
Published in: arXiv preprint arXiv:2504.18085, Issue 25 April 2025, 2025
Publisher: Cornell University
DOI: 10.48550/arxiv.2504.18085

Epistemic Wrapping for Uncertainty Quantification

Author(s): M. Sultana, N. Yorke-Smith, K. Wang, S.K. Manchingal, M. Mubashar, F. Cuzzolin
Published in: arXiv preprint arXiv:2505.02277, Issue 4 May 2025, 2025
Publisher: Cornell University
DOI: 10.48550/arxiv.2505.02277

Credal and Interval Deep Evidential Classifications

Author(s): M. Caprio, S.K. Manchingal, F. Cuzzolin
Published in: arXiv preprint arXiv:2512.05526, Issue December 5 2025, 2025
Publisher: Cornell University
DOI: 10.48550/arxiv.2512.05526

An introduction to optimization under uncertainty -- A short survey

Author(s): Shariatmadar, Keivan; Wang, Kaizheng; Hubbard, Calvin R.; Hallez, Hans; Moens, David
Published in: arXiv report arxiv.2212.00862, Issue 1 Dec 2022, 2022
Publisher: Cornell University
DOI: 10.48550/arxiv.2212.00862

Sufficient Decision Proxies For Decision-Focused Learning

Author(s): Schutte, N., Veviurko, G., Postek, K., and Yorke-Smith, N.
Published in: arXiv preprint, Issue May 2025, 2025
Publisher: Cornell University
DOI: 10.48550/arxiv.2505.03953

Credal learning theory

Author(s): M. Caprio, M. Sultana, E. Elia and F. Cuzzolin
Published in: arXiv preprint arXiv:2402.00957, Issue 2 May 2024, 2024
Publisher: Cornell University
DOI: 10.48550/arxiv.2402.00957

Reasoning with random sets: An agenda for the future

Author(s): F. Cuzzolin
Published in: arXiv preprint arXiv:2401.09435, Issue 19 Dec 2023, 2024
Publisher: Cornell University
DOI: 10.48550/arxiv.2401.09435

The intersection probability: betting with probability intervals

Author(s): Fabio Cuzzolin
Published in: arXiv preprint arxiv.2201.01729, Issue 5 January 2022, 2022
Publisher: Cornell University
DOI: 10.48550/arxiv.2201.01729

Uncertainty measures: The big picture

Author(s): Fabio Cuzzolin
Published in: arXiv preprint arxiv.2104.06839, Issue 14 April 2021, 2021
Publisher: Cornell University
DOI: 10.48550/arxiv.2104.06839

Direct Interval Propagation Methods using Neural-Network Surrogates for Uncertainty Quantification in Physical Systems Surrogate Model

Author(s): Ghifari Adam Faza, Jolan Wauters, Fabio Cuzzolin, Hans Hallez, David Moens
Published in: SSRN Preprint, Issue November 26 2025, 2025
Publisher: Elsevier BV
DOI: 10.2139/ssrn.5812330

CreINNs: Credal-Set Interval Neural Networks for Uncertainty Estimation in Classification Tasks

Author(s): Kaizheng Wang, Keivan Shariatmadar, Shireen Kudukkil Manchingal, Fabio Cuzzolin, David Moens, Hans Hallez
Published in: arXiv preprint arXiv:2401.05043, Issue 2 Feb 2024, 2024
Publisher: Cornell University
DOI: 10.48550/arxiv.2401.05043

Epistemic Artificial Intelligence is Essential for Machine Learning Models to Truly 'Know When They Do Not Know'

Author(s): S.K. Manchingal, A. Bradley, J.F.P. Kooij, K. Shariatmadar, N. Yorke-Smith, F. Cuzzolin
Published in: arXiv preprint arXiv:2505.04950, Issue 8 May 2025, 2025
Publisher: Cornell University
DOI: 10.48550/arxiv.2505.04950

Path Planning Problem under non-probabilistic Uncertainty

Author(s): K. Shariatmadar
Published in: arXiv preprint arXiv.2212.01388, Issue 1 Dec 2022, 2022
Publisher: Cornell University
DOI: 10.48550/arxiv.2212.01388

Vision in adverse weather: Augmentation using CycleGANs with various object detectors for robust perception in autonomous racing

Author(s): Izzeddin Teeti, Valentina Musat, Salman Khan, Alexander Rast, Fabio Cuzzolin, Andrew Bradley
Published in: arXiv preprint arxiv.2201.03246, Issue 11 January 2022, 2022
Publisher: Cornell University
DOI: 10.48550/arxiv.2201.03246

Generalisation of Total Uncertainty in AI: A Theoretical Study

Author(s): K. Shariatmadar
Published in: arXiv preprint arXiv.2408.00946, Issue 1 Aug 2024, 2024
Publisher: Cornell University
DOI: 10.48550/arxiv.2408.00946

Learning Credal Ensembles via Distributionally Robust Optimization

Author(s): K. Wang, G.A. Faza, F. Cuzzolin, S.L. Chau, D. Moens, H. Hallez
Published in: arXiv preprint arXiv:2602.08470, Issue February 9 2026, 2026
Publisher: Cornell University
DOI: 10.48550/arxiv.2602.08470

A geometric approach to conditioning belief functions

Author(s): Fabio Cuzzolin
Published in: arXiv preprint arxiv.2104.10651, Issue 21 April 2021, 2021
Publisher: Cornell University
DOI: 10.48550/arxiv.2104.10651

Conference proceedings (31)

Credal Ensemble Distillation for Uncertainty Quantification

Author(s): K. Wang, F. Cuzzolin, D. Moens, H. Hallez
Published in: 40th Annual AAAI Conference on Artificial Intelligence (AAAI 2026), Issue February 20 2026, 2026
Publisher: AAAI Press
DOI: 10.48550/arxiv.2511.13766

On the Estimation of Image-Matching Uncertainty in Visual Place Recognition

Author(s): Mubariz Zaffar, Liangliang Nan, Julian F. P. Kooij
Published in: 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Issue 31, 2024, Page(s) 17743-17753
Publisher: IEEE
DOI: 10.1109/cvpr52733.2024.01680

Bayesian Deep Q-Learning via Sequential Monte Carlo

Author(s): Pascal Van der Vaart, Matthijs T. J. Spaan, Neil Yorke-Smith
Published in: 16th European Workshop on Reinforcement Learning (EWRL 2023), Issue 14 Sep 2023, 2023
Publisher: EWRL
DOI: 10.5281/zenodo.14245136

Bayesian Model-Free Deep Reinforcement Learning

Author(s): Pascal van der Vaart
Published in: AAMAS '24: Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, Issue 6 May 2024, 2024, Page(s) 2782-2784
Publisher: IFAAMAS
DOI: 10.5555/3635637.3663285

Incentives for Accurate Energy Predictions: How to Reduce Epistemic Uncertainty

Author(s): Roland Saur, Han La Poutré, Neil Yorke-Smith
Published in: The 15th ACM International Conference on Future and Sustainable Energy Systems, Issue June 4 2024, 2025, Page(s) 192-202
Publisher: ACM
DOI: 10.1145/3632775.3661956

A Hybrid Graph Network for Complex Activity Detection in Video

Author(s): Salman Khan, Izzeddin Teeti, Andrew Bradley, Mohamed Elhoseiny, Fabio Cuzzolin
Published in: 2024 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Issue 32, 2024, Page(s) 6748-6758
Publisher: IEEE
DOI: 10.1109/wacv57701.2024.00662

Value Improved Actor Critic Algorithms

Author(s): Y. Oren, M. A. Zanger, P. R. van der Vaart, M. M. Çelikok, W. Böhmer, and M. T. J. Spaan
Published in: The Thirty‑ninth Annual Conference on Neural Information Processing Systems (NeurIPS), Issue 2 December 2025, 2025
Publisher: NeurIPS
DOI: 10.48550/arxiv.2406.01423

Diverse Projection Ensembles for Distributional Reinforcement Learning

Author(s): Zanger, Moritz A.; Böhmer, Wendelin; Spaan, Matthijs T. J.
Published in: 16th European Workshop on Reinforcement Learning (EWRL 2023), Issue 14 Sep 2023, 2023
Publisher: EWRL
DOI: 10.48550/arxiv.2306.07124

A Unified Evaluation Framework for Epistemic Predictions

Author(s): S.K. Manchingal, M. Mubashar, K. Wang, F. Cuzzolin
Published in: The 28th International Conference on Artificial Intelligence and Statistics, AISTATS 25, Issue 3 May 2025, 2025
Publisher: The Proceedings of Machine Learning Research
DOI: 10.48550/arxiv.2501.16912

Bayesian Ensembles for Exploration in Deep Q-Learning

Author(s): Pascal Van der Vaart, Neil Yorke-Smith, Matthijs T. J. Spaan
Published in: Sixteenth Workshop on Adaptive and Learning Agents (ALA 2024), Issue 6 May 2024, 2024, Page(s) 2528 - 2530
Publisher: Adaptive Learning Agents Workshop 2024
DOI: 10.5555/3635637.3663216

Interval Reduced Order Surrogate Modelling Framework for Uncertainty Quantification

Author(s): Ghifari A. Faza, Keivan Shariatmadar, Hans Hallez, David Moens
Published in: AIAA SCITECH 2024 Forum, Issue 4 Jan 2024, 2024
Publisher: American Institute of Aeronautics and Astronautics
DOI: 10.2514/6.2024-0387

A generalisation of the Bellman Equation in Epistemic Reinforcement Learning

Author(s): K. Shariatmadar, J. Golub, A. Faza, D. Moens
Published in: XAI workshop, Issue May 23 2024, 2024
Publisher: Eindhoven Artificial Intelligence Systems Institute
DOI: 10.5281/zenodo.14217032

Epistemic Deep Learning

Author(s): Shireen Kudukkil Manchingal, Fabio Cuzzolin
Published in: ICML 2022 Workshop on Distribution-Free Uncertainty Quantification, Issue 23 Jul 2022, 2022
Publisher: ICML
DOI: 10.48550/arxiv.2206.07609

Diverse Projection Ensembles for Distributional Reinforcement Learning

Author(s): Moritz Akiya Zanger, Wendelin Boehmer, Matthijs T. J. Spaan
Published in: The Twelfth International Conference on Learning Representations (ICLR 2024), Issue 7 May 2024, 2024
Publisher: ICLR
DOI: 10.5281/zenodo.14236990

How Ensembles of Distilled Policies Improve Generalisation in Reinforcement Learning

Author(s): M. Weltevrede, M. A. Zanger, M. T. J. Spaan, and W. Böhmer.
Published in: The Thirty‑ninth Annual Conference on Neural Information Processing Systems (NeurIPS),, Issue 2 December 2025, 2025
Publisher: NeurIPS
DOI: 10.48550/arxiv.2505.16581

Credal Deep Ensembles for Uncertainty Quantification

Author(s): Fabio Cuzzolin, Hans Hallez, Shireen Manchingal, David Moens, Keivan Shariatmadar, Kaizheng Wang
Published in: Advances in Neural Information Processing Systems 37, Issue 10 December 2024, 2025, Page(s) 79540-79572
Publisher: Neural Information Processing Systems Foundation, Inc. (NeurIPS)
DOI: 10.52202/079017-2525

Uncertainty-Aware Autonomous Vehicles: Predicting the Road Ahead

Author(s): S.K. Manchingal, A. Amaritei, M. Gohad, M. Sultana, J.F.P. Kooij, F. Cuzzolin, A. Bradley
Published in: IEEE International Conference on Robotics & Automation (ICRA 2026), Issue June 1 2026, 2026
Publisher: IEEE International Conference on Robotics & Automation (ICRA)
DOI: 10.48550/arxiv.2510.22680

Optimization Under Epistemic Uncertainty With a Focus on Decision-Focused Learning

Author(s): Noah Schutte
Published in: Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence, Issue 3-9 Aug 2024, 2024, Page(s) 8504-8505
Publisher: International Joint Conferences on Artificial Intelligence Organization
DOI: 10.24963/ijcai.2024/967

Science of AI - Total Uncertainty in AI

Author(s): Keivan Shariatmadar
Published in: Leuven.AI Workshop, Issue Jun 3 2024, 2024
Publisher: KU Leuven
DOI: 10.5281/zenodo.14223938

Credal Wrapper of Model Averaging for Uncertainty Estimation on Out-Of-Distribution Detection

Author(s): K. Wang, F. Cuzzolin, K. Shariatmadar, D. Moens, H. Hallez
Published in: 3th International Conference on Learning Representations (ICLR 2025), Issue 24 April 2025, 2025, ISBN 9798331320850
Publisher: International Conference on Learning Representations (ICLR), POD Curran Associates, Inc.
DOI: 10.48550/arxiv.2405.15047

Universal Value-Function Uncertainties

Author(s): M.A. Zanger, M. Weltevrede, Y. Oren, P.R. Van der Vaart, C. Horsch, W. Bohmer, M.T.J. Spaan
Published in: The 14th International Conference on Learning Representations (ICLR 2026), Issue April 23 2026, 2026
Publisher: International Conference on Learning Representations (ICLR)
DOI: 10.48550/arxiv.2505.21119

Robust Losses for Decision-Focused Learning

Author(s): Noah Schutte, Krzysztof Postek, Neil Yorke-Smith
Published in: Proceedings of the Thirty-ThirdInternational Joint Conference on Artificial Intelligence, Issue 3-9 Aug 2024, 2024, Page(s) 4868-4875
Publisher: International Joint Conferences on Artificial Intelligence Organization
DOI: 10.24963/ijcai.2024/538

Random-Set Convolutional Neural Network (RS-CNN) for Epistemic Deep Learning

Author(s): Shireen Kudukkil Manchingal, Muhammad Mubashar, Kaizheng Wang, Keivan Shariatmadar, Fabio Cuzzolin
Published in: 13th International Conference on Learning Representations (ICLR 2025), Issue 24 April 2025, 2025, ISBN 9798331320850
Publisher: International Conference on Learning Representations (ICLR), POD Curran Associates, Inc.
DOI: 10.48550/arxiv.2307.05772

YOLO-Z: Improving small object detection in YOLOv5 for autonomous vehicles

Author(s): Aduen Benjumea, Izzeddin Teeti, Fabio Cuzzolin, Andrew Bradley
Published in: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Issue 23 December 2021, 2021
Publisher: IEEE
DOI: 10.48550/arxiv.2112.11798

E-MCTS: Deep Exploration in Model-Based Reinforcement Learning by Planning with Epistemic Uncertainty

Author(s): Oren, Yaniv; Spaan, Matthijs T. J.; Böhmer, Wendelin
Published in: 13th International Conference on Learning Representations (ICLR 2025), Issue 24 April 2025, 2025, ISBN 9798331320850
Publisher: International Conference on Learning Representations (ICLR), POD Curran Associates, Inc.
DOI: 10.48550/arxiv.2210.13455

Contextual Similarity Distillation: Ensemble Uncertainties with a Single Model

Author(s): M.A. Zanger, P.R. Van der Vaart, W. Boehmer, M.T.J. Spaan
Published in: The 14th International Conference on Learning Representations (ICLR 2026), Issue April 23 2026, 2026
Publisher: International Conference on Learning Representations (ICLR)
DOI: 10.48550/arxiv.2503.11339

Credal Learning Theory

Author(s): Michele Caprio, Fabio Cuzzolin, Eleni Elia, Maryam Sultana
Published in: Advances in Neural Information Processing Systems 37, Issue 10 December 2024, 2025, Page(s) 38665-38694
Publisher: Neural Information Processing Systems Foundation, Inc. (NeurIPS)
DOI: 10.52202/079017-1221

Epistemic Bellman Operators

Author(s): Pascal R. Van der Vaart, Matthijs T. J. Spaan, Neil Yorke-Smith
Published in: Proceedings of the AAAI Conference on Artificial Intelligence, Issue 39, 2025, Page(s) 20973-20981, ISSN 2374-3468
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
DOI: 10.1609/aaai.v39i20.35393

Multi-weather city: Adverse weather stacking for autonomous driving

Author(s): Valentina Mușat, Ivan Fursa, Paul Newman, Fabio Cuzzolin, Andrew Bradley
Published in: 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), Issue 11-17 Oct 2021, 2022
Publisher: IEEE
DOI: 10.1109/iccvw54120.2021.00325

Navigating the Waters of Object Detection: Evaluating the Robustness of Real-time Object Detection Models for Autonomous Surface Vehicles

Author(s): Yunjia Wang, Kaizheng Wang, Zihao Zhang, Jeroen Boydens, Davy Pissoort, Mathias Verbeke
Published in: 2024 IEEE Conference on Artificial Intelligence (CAI), Issue June 25 2024, 2024, Page(s) 985-992
Publisher: IEEE
DOI: 10.1109/cai59869.2024.00180

ROAD-R: The Autonomous Driving Dataset with Logical Requirements

Author(s): E. Giunchiglia, M. Stoian, S. Khan, F. Cuzzolin and T. Lukasiewicz
Published in: IJCAI 2022 - Workshop on Artificial Intelligence for Autonomous Driving (AI4AD 2022), Issue 23 Jul 2022, 2022
Publisher: International Joint Conferences on Artificial Intelligence Organization
DOI: 10.48550/arxiv.2210.01597

Monographic books (1)

Epistemic Uncertainty in Artificial Intelligence

Author(s): Fabio Cuzzolin, Maryam Sultana
Published in: Lecture Notes in Computer Science, Issue 23 Apr 2024, 2024, ISBN 978-3-031-57963-9
Publisher: Springer Nature
DOI: 10.1007/978-3-031-57963-9

Thesis and dissertations (1)

Uncertainty in Reinforcement Learning and its Application to Scheduling Problems in Manufacturing

Author(s): J. Golub, A. Faza, K. Shariatmadar, D. Moens
Published in: MSc dissertation, 2024
Publisher: KU Leuven
DOI: 10.5281/zenodo.14223900

Book chapters (1)

Improving Metaheuristic Efficiency for Stochastic Optimization by Sequential Predictive Sampling

Author(s): Noah Schutte, Krzysztof Postek, Neil Yorke-Smith
Published in: Lecture Notes in Computer Science, Integration of Constraint Programming, Artificial Intelligence, and Operations Research, Issue 25 May 2024, 2024, Page(s) 158-175
Publisher: Springer Nature Switzerland
DOI: 10.1007/978-3-031-60599-4_10