Auswahl der wissenschaftlichen Literatur zum Thema „Belief merging“
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Zeitschriftenartikel zum Thema "Belief merging"
Haret, Adrian, Martin Lackner, Andreas Pfandler und Johannes P. Wallner. „Proportional Belief Merging“. Proceedings of the AAAI Conference on Artificial Intelligence 34, Nr. 03 (03.04.2020): 2822–29. http://dx.doi.org/10.1609/aaai.v34i03.5671.
Der volle Inhalt der QuelleLiberatore, Paolo. „Belief Merging by Examples“. ACM Transactions on Computational Logic 17, Nr. 2 (28.03.2016): 1–38. http://dx.doi.org/10.1145/2818645.
Der volle Inhalt der QuelleMata Díaz, Amílcar, und Ramón Pino Pérez. „Impossibility in belief merging“. Artificial Intelligence 251 (Oktober 2017): 1–34. http://dx.doi.org/10.1016/j.artint.2017.06.003.
Der volle Inhalt der QuelleAravanis, Theofanis I. „Collective Belief Revision“. Journal of Artificial Intelligence Research 78 (29.12.2023): 1221–47. http://dx.doi.org/10.1613/jair.1.15745.
Der volle Inhalt der QuelleGauwin, O., S. Konieczny und P. Marquis. „Conciliation through Iterated Belief Merging“. Journal of Logic and Computation 17, Nr. 5 (01.10.2007): 909–37. http://dx.doi.org/10.1093/logcom/exm047.
Der volle Inhalt der QuelleGabbay, D., O. Rodrigues und G. Pigozzi. „Connections between Belief Revision, Belief Merging and Social Choice“. Journal of Logic and Computation 19, Nr. 3 (21.05.2009): 445–46. http://dx.doi.org/10.1093/logcom/exn013.
Der volle Inhalt der QuelleKonieczny, Sébastien. „Belief base merging as a game“. Journal of Applied Non-Classical Logics 14, Nr. 3 (Januar 2004): 275–94. http://dx.doi.org/10.3166/jancl.14.275-294.
Der volle Inhalt der QuelleTran, Trong Hieu, Quoc Bao Vo und Thi Hong Khanh Nguyen. „On the Belief Merging by Negotiation“. Procedia Computer Science 35 (2014): 147–55. http://dx.doi.org/10.1016/j.procs.2014.08.094.
Der volle Inhalt der QuelleSchwind, Nicolas, Sébastien Konieczny und Pierre Marquis. „Belief base rationalization for propositional merging“. Journal of Logic and Computation 28, Nr. 7 (Oktober 2018): 1601–34. http://dx.doi.org/10.1093/logcom/exy029.
Der volle Inhalt der QuelleMa, Jianbing, Weiru Liu und Anthony Hunter. „Inducing Probability Distributions from Knowledge Bases with (In)dependence Relations“. Proceedings of the AAAI Conference on Artificial Intelligence 24, Nr. 1 (03.07.2010): 339–44. http://dx.doi.org/10.1609/aaai.v24i1.7588.
Der volle Inhalt der QuelleDissertationen zum Thema "Belief merging"
Lin, Qiuming. „Viewpoints consistency management using belief merging operators“. Access electronically, 2004. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20041222.125858/index.html.
Der volle Inhalt der QuelleMa, Truong-Thanh. „Sur la fusion d'ontologies à domaine ouvert“. Electronic Thesis or Diss., Artois, 2022. http://www.theses.fr/2022ARTO0406.
Der volle Inhalt der QuelleThe subject of the thesis is ontology merging, an approach for integrating various ontology sources into a unique one that handles emerging conflicts. This dissertation takes inspiration from the belief merging theory for merging ontologies to produce a consistent and unified knowledge base. We propose the three following contributions.The first one is a semantic-based merging approach. In particular, a model-based merging strategy mainly focuses on handling semantic conflicts. A semantic conflict, which is not necessarily logical, is knowledge represented in many different or opposite ways. We also propose a formal model characterization and show the method's effectiveness with an experimental evaluation.The second approach proposes a new framework to merge open-domain terminological knowledge. It leverages RCC5, a formalism for representing regions in a topological space and reasoning about their set-theoretic relationships. We propose a faithful translation of terminological knowledge from conflicting sources into region spaces. Here, we merge knowledge bases in this space and translate the outcome into the input sources' language. Our technique uses RCC5's expressivity and flexibility to deal with contradictory knowledge.The last contribution is a framework for evaluating ontology merging operators. The primary strategy starts with an original ontology to create noisy ontologies as datasets and use them to assess the merging operators. Then, we analyze merging operators' computation time effectiveness and ability to cover the original ontology. Finally, we experimented with practical ontologies to evaluate the merging operators
Zayrit, Karima. „Fusion de données imparfaites multi-sources : application à la spatialisation qualifiée des pratiques agricoles“. Thesis, Reims, 2015. http://www.theses.fr/2015REIMS041/document.
Der volle Inhalt der QuelleOur thesis is part of a regional project aiming the development of a community environmental information system for agricultural practices in the watershed of the Vesle. The objective of this observatory is 1) to understand the practices of responsible of the water resource pollution by pesticides from agriculture in the study area and 2) to provide relevant and sustainable tools to estimate their impacts. Our open issue deals with the consideration of imperfection in the process of merging multiple sources and imperfect data. Indeed, information on practices is not exhaustive and is not subject to return, so we need to build this knowledge through the combination of multiple sources and of varying quality by integrating imperfect information management information in the system. In this context, we propose methods for spatial reconstruction of information related to agricultural practices from the RPG remote sensing, field surveys and expert opinions, skilled reconstruction with an assessment of the quality of the information. Furthermore, we propose a conceptual modeling of agronomic entities' imperfect information system building on UML and PERCEPTORY.We provide tools and models of representation of imperfect information from the various sources of information using fuzzy sets and the belief function theory and integrate these models into the computation of agri-environmental indicators such as TFI and ASQ
Bücher zum Thema "Belief merging"
Sass, Louis A., und Elizabeth Pienkos. Delusion. Herausgegeben von K. W. M. Fulford, Martin Davies, Richard G. T. Gipps, George Graham, John Z. Sadler, Giovanni Stanghellini und Tim Thornton. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199579563.013.0039.
Der volle Inhalt der QuelleBullock, Kim, und John J. Barry. Psychiatric Factors. Herausgegeben von Barbara A. Dworetzky und Gaston C. Baslet. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190265045.003.0003.
Der volle Inhalt der QuelleBuchteile zum Thema "Belief merging"
Le, Thi Thanh Luu, und Trong Hieu Tran. „Belief Merging for Possibilistic Belief Bases“. In Advanced Computational Methods for Knowledge Engineering, 370–80. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-38364-0_33.
Der volle Inhalt der QuelleViana, Henrique, und João Alcântara. „Sufficientarian Propositional Belief Merging“. In Multi-Agent Systems and Agreement Technologies, 421–35. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59294-7_35.
Der volle Inhalt der QuelleGeorgatos, Konstantinos. „Graph-Based Belief Merging“. In Logic, Rationality, and Interaction, 102–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-48561-3_9.
Der volle Inhalt der QuelleRedl, Christoph, Thomas Eiter und Thomas Krennwallner. „Declarative Belief Set Merging Using Merging Plans“. In Practical Aspects of Declarative Languages, 99–114. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18378-2_10.
Der volle Inhalt der QuelleTran, Trong Hieu, Quoc Bao Vo und Ryszard Kowalczyk. „Merging Belief Bases by Negotiation“. In Knowledge-Based and Intelligent Information and Engineering Systems, 200–209. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23851-2_21.
Der volle Inhalt der QuelleCojan, Julien, und Jean Lieber. „Belief Merging-Based Case Combination“. In Case-Based Reasoning Research and Development, 105–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02998-1_9.
Der volle Inhalt der QuellePozos-Parra, Pilar, Laurent Perrussel und Jean Marc Thevenin. „Belief Merging Using Normal Forms“. In Advances in Artificial Intelligence, 40–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25324-9_4.
Der volle Inhalt der QuelleBenferhat, Salem, Julien Hué, Sylvain Lagrue und Julien Rossit. „Merging Interval-Based Possibilistic Belief Bases“. In Lecture Notes in Computer Science, 447–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33362-0_34.
Der volle Inhalt der QuelleDestercke, Sebastien, Didier Dubois und Eric Chojnacki. „Cautious Conjunctive Merging of Belief Functions“. In Lecture Notes in Computer Science, 332–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75256-1_31.
Der volle Inhalt der QuelleNguyen, Thi Hong Khanh, Trong Hieu Tran, Tran Van Nguyen und Thi Thanh Luu Le. „Merging Possibilistic Belief Bases by Argumentation“. In Intelligent Information and Database Systems, 24–34. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-54472-4_3.
Der volle Inhalt der QuelleKonferenzberichte zum Thema "Belief merging"
Everaere, Patricia, Chouaib Fellah, Sébastien Konieczny und Ramón Pino Pérez. „Borda, Cancellation and Belief Merging“. In 18th International Conference on Principles of Knowledge Representation and Reasoning {KR-2021}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/kr.2021/28.
Der volle Inhalt der QuelleAravanis, Theofanis. „Incorporating Belief Merging into Relevance-Sensitive Belief Structures“. In PCI 2022: 26th Pan-Hellenic Conference on Informatics. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3575879.3575959.
Der volle Inhalt der QuelleEveraere, Patricia, Chouaib Fellah, Sébastien Konieczny und Ramón Pino Pérez. „Weighted Merging of Propositional Belief Bases“. In 20th International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/kr.2023/22.
Der volle Inhalt der QuelleEveraere, Patricia, Sebastien Konieczny und Pierre Marquis. „Belief Merging Operators as Maximum Likelihood Estimators“. In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/244.
Der volle Inhalt der QuelleMata Diaz, Amilcar, und Ramon Pino Perez. „Impossibility in Belief Merging (Extended Abstract)“. In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/799.
Der volle Inhalt der QuelleBorja Macías, Verónica, und Pilar Pozos Parra. „Model-based belief merging without distance measures“. In the 6th international joint conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1329125.1329312.
Der volle Inhalt der QuelleSingleton, Joseph, und Richard Booth. „Who’s the Expert? On Multi-source Belief Change“. In 19th International Conference on Principles of Knowledge Representation and Reasoning {KR-2022}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/kr.2022/33.
Der volle Inhalt der QuelleSchwind, Nicolas, und Sébastien Konieczny. „Non-Prioritized Iterated Revision: Improvement via Incremental Belief Merging“. In 17th International Conference on Principles of Knowledge Representation and Reasoning {KR-2020}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/kr.2020/76.
Der volle Inhalt der QuelleFischer, Johannes, Etienne Buhrle, Danial Kamran und Christoph Stiller. „Guiding Belief Space Planning with Learned Models for Interactive Merging“. In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2022. http://dx.doi.org/10.1109/itsc55140.2022.9922488.
Der volle Inhalt der QuelleRiegler, Erwin, Gunvor Elisabeth Kirkelund, Carles Navarro Manchon und Bernard Henri Fleury. „Merging belief propagation and the mean field approximation: A free energy approach“. In Iterative Information Processing (ISTC). IEEE, 2010. http://dx.doi.org/10.1109/istc.2010.5613851.
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