Conversational Human-Aware Technology for Optimisation

Informazioni relative al progetto

CHAT-Opt

ID dell’accordo di sovvenzione: 101002802

DOI

10.3030/101002802

Data della firma CE 30 Marzo 2021

Data di avvio 1 Giugno 2021

Data di completamento 31 Maggio 2026

Finanziato da

• EXCELLENT SCIENCE - European Research Council (ERC)

Costo totale

€ 1 893 125,00

Contributo UE

€ 1 893 125,00

Coordinato da

KATHOLIEKE UNIVERSITEIT LEUVEN
Belgium

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Open Research Data Pilot (1)

Data Management Plan

Pubblicazioni

Other (3)

Things we underestimated while developing the CPMpy constraint modelling library

Autori: Tias Guns, Dimosthenis Tsouros
Pubblicato in: The 22nd workshop on Constraint Modelling and Reformulation, 2023
Editore: online: https://modref.github.io/ModRef2023.html

Holy Grail 2.0: From Natural Language to Constraint Models

Autori: Dimosthenis Tsouros, Helene Verhaeghe, Serdar Kadioglu, Tias Guns
Pubblicato in: Progress Toward the Holy Grail workshop at CP2023, 2023
Editore: Online: https://freuder.wordpress.com/progress-towards-the-holy-grail-workshops/pthg-23-the-sixth-workshop-on-progress-towards-the-holy-grail/
DOI: 10.48550/arxiv.2308.01589

Sudoku Assistant - An AI-powered app to help solve pen-and-paper Sudokus

Autori: Tias Guns, Milan Pesa, Maxime Mulamba, Ignace Bleukx, Emilio Gamba, and Senne Berden
Pubblicato in: BNAIC/BeNeLearn AI & ML conference for Belgium, Netherlands & Luxembourg, 2022
Editore: Benelux Association for Artificial Intelligence (BNVKI)

Conference proceedings (13)

Learning to Learn in Interactive Constraint Acquisition

Autori: Dimosthenis Tsouros, Senne Berden, Tias Guns
Pubblicato in: Proceedings of the AAAI Conference on Artificial Intelligence, Numero 8, 2024, Pagina/e 8154-8162
Editore: AAAI Press
DOI: 10.1609/aaai.v38i8.28655

Simplifying Step-wise Explanation Sequences

Autori: Ignace Bleukx, Jo Devriendt, Emilio Gamba, Bart Bogaerts, Tias Guns
Pubblicato in: Leibniz International Proceedings in Informatics (LIPIcs), Numero Volume 280, 2023, Pagina/e 11:1 - 11:20
Editore: Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
DOI: 10.4230/lipics.cp.2023.11

Guided Bottom-Up Interactive Constraint Acquisition

Autori: Dimosthenis Tsouros, Senne Berden, Tias Guns
Pubblicato in: Leibniz International Proceedings in Informatics (LIPIcs), 2023, Pagina/e 36:1 - 36:20
Editore: Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
DOI: 10.4230/lipics.cp.2023.36

An Efficient Structured Perceptron for NP-Hard Combinatorial Optimization Problems

Autori: Bastián Véjar, Gaël Aglin, Ali İrfan Mahmutoğulları, Siegfried Nijssen, Pierre Schaus, Tias Guns
Pubblicato in: Integration of Constraint Programming, Artificial Intelligence, and Operations Research, proceedings, 2024, Pagina/e 253--262, ISBN 978-3-031-60599-4
Editore: Springer Nature Switzerland
DOI: 10.1007/978-3-031-60599-4_17

Model-Based Algorithm Configuration with Adaptive Capping and Prior Distributions

Autori: Ignace Bleukx, Senne Berden, Lize Coenen, Nicholas Decleyre, Tias Guns
Pubblicato in: Integration of Constraint Programming, Artificial Intelligence, and Operations Research. Lecture Notes in Computer Science, Numero volume 13292, 2022, Pagina/e 64-73
Editore: Springer
DOI: 10.1007/978-3-031-08011-1_6

Learning Constraint Programming Models from Data Using Generate-And-Aggregate

Autori: Mohit Kumar, Samuel Kolb, Tias Guns
Pubblicato in: 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Numero Volume 235, 2022, Pagina/e 29:1 - 29:16, ISBN 978-3-95977-240-2
Editore: Schloss Dagstuhl – Leibniz-Zentrum für Informatik (lzi)
DOI: 10.4230/lipics.cp.2022.29

Decision-Focused Learning: Through the Lens of Learning to Rank

Autori: Jayanta Mandi, Víctor Bucarey, Maxime Mulamba Ke Tchomba, Tias Guns
Pubblicato in: Proceedings of the 39th International Conference on Machine Learning;, Numero Vol. 162, 2022, Pagina/e 14935 - 14947
Editore: PMLR

Mutational Fuzz Testing for Constraint Modeling Systems.

Autori: Vanroose, Wout; Bleukx, Ignace; Devriendt, jo; Tsouros, dimos; Verhaeghe, hélène; Guns, tias
Pubblicato in: 30th International Conference on Principles and Practice of Constraint Programming (CP 2024), 2024, Pagina/e 29:1--29:25, ISBN 978-3-95977-336-2
Editore: "Schloss Dagstuhl -- Leibniz-Zentrum f{\""u}r Informatik"
DOI: 10.4230/lipics.cp.2024.29

Sudoku Assistant – an AI-Powered App to Help Solve Pen-and-Paper Sudokus

Autori: Tias Guns, Emilio Gamba, Ignace Bleukx, Senne Berden, Milan Pesa
Pubblicato in: Proceedings of the AAAI Conference on Artificial Intelligence, Numero vol. 37 (13), 2023, Pagina/e 16440-16442
Editore: AAAI press
DOI: 10.1609/aaai.v37i13.27072

Contrastive Losses and Solution Caching for Predict-and-Optimize

Autori: Maxime Mulamba, Jayanta Mandi, Michelangelo Diligenti, Michele Lombardi, Victor Bucarey, Tias Guns
Pubblicato in: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021, Pagina/e 2833-2840
Editore: ijcai.org
DOI: 10.24963/ijcai.2021/390

Constraint Modelling with LLMs Using In-Context Learning.

Autori: Michailidis, Kostis; Tsouros, Dimos; Guns, Tias
Pubblicato in: 30th International Conference on Principles and Practice of Constraint Programming (CP 2024), 2024, Pagina/e 20:1--20:27, ISBN 978-3-95977-336-2
Editore: "Schloss Dagstuhl -- Leibniz-Zentrum f{\""u}r Informatik"
DOI: 10.4230/lipics.cp.2024.20

Efficiently Explaining CSPs with Unsatisfiable Subset Optimization

Autori: Emilio Gamba, Bart Bogaerts, Tias Guns
Pubblicato in: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, 2021, Pagina/e 1381-1388
Editore: ijcai.org
DOI: 10.24963/ijcai.2021/191

Learning MAX-SAT Models from Examples Using Genetic Algorithms and Knowledge Compilation

Autori: Senne Berden, Mohit Kumar, Samuel Kolb, Tias Guns:
Pubblicato in: 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Numero Volume 235, 2022, Pagina/e 8:1-8:16, ISBN 978-3-95977-240-2
Editore: Schloss Dagstuhl – Leibniz-Zentrum für Informatik (lzi)
DOI: 10.4230/lipics.cp.2022.8

Peer reviewed articles (5)

Probability Estimation and Structured Output Prediction for Learning Preferences in Last Mile Delivery

Autori: Rocsildes Canoy, Victor Bucarey, Jayanta Mandi, Maxime Mulamba, Yves Molenbruch, Tias Guns
Pubblicato in: Computers & Industrial Engineering, Numero 189, 2023, Pagina/e 109932, ISSN 0360-8352
Editore: Pergamon Press Ltd.
DOI: 10.2139/ssrn.4363937

Perception-based constraint solving for sudoku images

Autori: Maxime Mulamba, Jayanta Mandi, Ali İrfan Mahmutoğulları, Tias Guns
Pubblicato in: Constraints, Numero 29, 2024, Pagina/e 112-151, ISSN 1383-7133
Editore: Kluwer Academic Publishers
DOI: 10.1007/s10601-024-09372-9

Decision-Focused Learning: Foundations, State of the Art, Benchmark and Future Opportunities

Autori: Jayanta Mandi, James Kotary, Senne Berden, Maxime Mulamba, Victor Bucarey, Tias Guns, Ferdinando Fioretto
Pubblicato in: Journal of Artificial Intelligence Research, Numero 80, 2024, Pagina/e 1623-1701, ISSN 1076-9757
Editore: Morgan Kaufmann Publishers, Inc.
DOI: 10.1613/jair.1.15320

Learn and route: learning implicit preferences for vehicle routing

Autori: Rocsildes Canoy, Víctor Bucarey, Jayanta Mandi, Tias Guns
Pubblicato in: Constraints, Numero vol. 28; iss. 3, 2023, Pagina/e 363 - 396, ISSN 1383-7133
Editore: Kluwer Academic Publishers
DOI: 10.1007/s10601-023-09363-2

Generalizing Constraint Models in Constraint Acquisition

Autori: Dimos Tsouros, Senne Berden, Steven Prestwich, Tias Guns
Pubblicato in: Proceedings of the AAAI Conference on Artificial Intelligence, Numero 39, 2025, Pagina/e 11362-11371, ISSN 2374-3468
Editore: Association for the Advancement of Artificial Intelligence (AAAI)
DOI: 10.1609/aaai.v39i11.33236