Literatura científica selecionada sobre o tema "Belief merging"
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Artigos de revistas sobre o assunto "Belief merging"
Haret, Adrian, Martin Lackner, Andreas Pfandler e Johannes P. Wallner. "Proportional Belief Merging". Proceedings of the AAAI Conference on Artificial Intelligence 34, n.º 03 (3 de abril de 2020): 2822–29. http://dx.doi.org/10.1609/aaai.v34i03.5671.
Texto completo da fonteLiberatore, Paolo. "Belief Merging by Examples". ACM Transactions on Computational Logic 17, n.º 2 (28 de março de 2016): 1–38. http://dx.doi.org/10.1145/2818645.
Texto completo da fonteMata Díaz, Amílcar, e Ramón Pino Pérez. "Impossibility in belief merging". Artificial Intelligence 251 (outubro de 2017): 1–34. http://dx.doi.org/10.1016/j.artint.2017.06.003.
Texto completo da fonteAravanis, Theofanis I. "Collective Belief Revision". Journal of Artificial Intelligence Research 78 (29 de dezembro de 2023): 1221–47. http://dx.doi.org/10.1613/jair.1.15745.
Texto completo da fonteGauwin, O., S. Konieczny e P. Marquis. "Conciliation through Iterated Belief Merging". Journal of Logic and Computation 17, n.º 5 (1 de outubro de 2007): 909–37. http://dx.doi.org/10.1093/logcom/exm047.
Texto completo da fonteGabbay, D., O. Rodrigues e G. Pigozzi. "Connections between Belief Revision, Belief Merging and Social Choice". Journal of Logic and Computation 19, n.º 3 (21 de maio de 2009): 445–46. http://dx.doi.org/10.1093/logcom/exn013.
Texto completo da fonteKonieczny, Sébastien. "Belief base merging as a game". Journal of Applied Non-Classical Logics 14, n.º 3 (janeiro de 2004): 275–94. http://dx.doi.org/10.3166/jancl.14.275-294.
Texto completo da fonteTran, Trong Hieu, Quoc Bao Vo e 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.
Texto completo da fonteSchwind, Nicolas, Sébastien Konieczny e Pierre Marquis. "Belief base rationalization for propositional merging". Journal of Logic and Computation 28, n.º 7 (outubro de 2018): 1601–34. http://dx.doi.org/10.1093/logcom/exy029.
Texto completo da fonteMa, Jianbing, Weiru Liu e Anthony Hunter. "Inducing Probability Distributions from Knowledge Bases with (In)dependence Relations". Proceedings of the AAAI Conference on Artificial Intelligence 24, n.º 1 (3 de julho de 2010): 339–44. http://dx.doi.org/10.1609/aaai.v24i1.7588.
Texto completo da fonteTeses / dissertações sobre o assunto "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.
Texto completo da fonteMa, Truong-Thanh. "Sur la fusion d'ontologies à domaine ouvert". Electronic Thesis or Diss., Artois, 2022. http://www.theses.fr/2022ARTO0406.
Texto completo da fonteThe 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.
Texto completo da fonteOur 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
Livros sobre o assunto "Belief merging"
Sass, Louis A., e Elizabeth Pienkos. Delusion. Editado por K. W. M. Fulford, Martin Davies, Richard G. T. Gipps, George Graham, John Z. Sadler, Giovanni Stanghellini e Tim Thornton. Oxford University Press, 2013. http://dx.doi.org/10.1093/oxfordhb/9780199579563.013.0039.
Texto completo da fonteBullock, Kim, e John J. Barry. Psychiatric Factors. Editado por Barbara A. Dworetzky e Gaston C. Baslet. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190265045.003.0003.
Texto completo da fonteCapítulos de livros sobre o assunto "Belief merging"
Le, Thi Thanh Luu, e 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.
Texto completo da fonteViana, Henrique, e 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.
Texto completo da fonteGeorgatos, 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.
Texto completo da fonteRedl, Christoph, Thomas Eiter e 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.
Texto completo da fonteTran, Trong Hieu, Quoc Bao Vo e 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.
Texto completo da fonteCojan, Julien, e 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.
Texto completo da fontePozos-Parra, Pilar, Laurent Perrussel e 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.
Texto completo da fonteBenferhat, Salem, Julien Hué, Sylvain Lagrue e 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.
Texto completo da fonteDestercke, Sebastien, Didier Dubois e 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.
Texto completo da fonteNguyen, Thi Hong Khanh, Trong Hieu Tran, Tran Van Nguyen e 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.
Texto completo da fonteTrabalhos de conferências sobre o assunto "Belief merging"
Everaere, Patricia, Chouaib Fellah, Sébastien Konieczny e 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.
Texto completo da fonteAravanis, 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.
Texto completo da fonteEveraere, Patricia, Chouaib Fellah, Sébastien Konieczny e 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.
Texto completo da fonteEveraere, Patricia, Sebastien Konieczny e 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.
Texto completo da fonteMata Diaz, Amilcar, e 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.
Texto completo da fonteBorja Macías, Verónica, e 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.
Texto completo da fonteSingleton, Joseph, e 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.
Texto completo da fonteSchwind, Nicolas, e 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.
Texto completo da fonteFischer, Johannes, Etienne Buhrle, Danial Kamran e 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.
Texto completo da fonteRiegler, Erwin, Gunvor Elisabeth Kirkelund, Carles Navarro Manchon e 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.
Texto completo da fonte