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Inference and Learning with Model Uncertainty in Probabilistic Logic Programs
Department of Computer Science, Leuven.AI, KU Leuven, Belgium.
Department of Computer Science, Leuven.AI, KU Leuven, Belgium.
Department of Computer Science, Leuven.AI, KU Leuven, Belgium.
Örebro University, School of Science and Technology. Department of Computer Science, Leuven.AI, KU Leuven, Belgium.ORCID iD: 0000-0002-6860-6303
2022 (English)In: Proceedings 38th International Conference on Logic Programming, Haifa, Israel, 31st July 2022 - 6th August 2022 / [ed] Yuliya Lierler; Jose F. Morales; Carmine Dodaro; Veronica Dahl; Martin Gebser; Tuncay Tekle, Open Publishing Association , 2022, Vol. 364, p. 153-155Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Open Publishing Association , 2022. Vol. 364, p. 153-155
Series
Electronic Proceedings in Theoretical Computer Science (EPTCS), E-ISSN 2075-2180 ; 364
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:oru:diva-109466ISI: 001080380400018OAI: oai:DiVA.org:oru-109466DiVA, id: diva2:1808453
Conference
38th International Conference on Logic Programming, Haifa, Israel, July 31 - August 6, 2022
Funder
Wallenberg AI, Autonomous Systems and Software Program (WASP)EU, Horizon 2020
Note

VV received funding from the Flemish Government under the "Onderzoeksprogramma Artificiele Intelligentie (AI) Vlaanderen" programme. VD is supported by an SB PhD fellowship at the Research Foundation -- Flanders [1SA5520N] . PZD has received support from the KU Leuven Special Research Fund. LDR is also funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant No [694980] ) and the Wallenberg AI, Autonomous Systems and Software Program. 

Available from: 2023-10-31 Created: 2023-10-31 Last updated: 2023-10-31Bibliographically approved

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De Raedt, Luc

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