Unified framework for implementing inaccurate knowledge in quantum symbolic artificial intelligence models

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UDC.coleccionInvestigaciónes_ES
UDC.conferenceTitleICAART 2025 - International Conference on Agents and Artificial Intelligencees_ES
UDC.departamentoCiencias da Computación e Tecnoloxías da Informaciónes_ES
UDC.endPage846es_ES
UDC.grupoInvLaboratorio de Investigación e Desenvolvemento en Intelixencia Artificial (LIDIA)es_ES
UDC.institutoCentroCITIC - Centro de Investigación de Tecnoloxías da Información e da Comunicaciónes_ES
UDC.startPage839es_ES
UDC.volume1es_ES
dc.contributor.authorMosqueira-Rey, Eduardo
dc.contributor.authorMagaz-Romero, Samuel
dc.contributor.authorMoret-Bonillo, Vicente
dc.date.accessioned2025-05-20T18:07:04Z
dc.date.available2025-05-20T18:07:04Z
dc.date.issued2025-02
dc.descriptionTrabajo presentado a: 17th International Conference on Agents and Artificial Intelligence, Porto, Portugal, 23-25 February 2025.es_ES
dc.description.abstract[Abstract]; Symbolic models of Artificial Intelligence are based on defining declarative knowledge that is connected through procedural knowledge forming symbolic graphs through which reasoning flows. Both declarative and procedural knowledge can be inaccurate, which has led to the definition of different models to represent this inaccuracy. Since the functioning of quantum computers is inherently probabilistic, it has been proposed to take advantage of this nature to implement inaccurate knowledge more effectively. In this paper, we present different models for implementing inaccurate knowledge in quantum computers and propose a unified framework to represent and implement the common features of all of them.es_ES
dc.description.sponsorshipThis work has been supported by the EU’s Horizon 2020 under project NEASQC (grant No 951821), the State Research Agency of the Spanish Government (Grant PID2023-147422OB-I00) and by the Xunta de Galicia (Grant ED431C 2022/44), supported by the EU European Regional Development Fund (ERDF). CITIC, as a center accredited for excellence within the Galician University System and a member of the CIGUS Network, receives subsidies from the Department of Education, Science, Universities, and Vocational Training of the Xunta de Galicia. Additionally, it is co-financed by the EU through the FEDER Galicia 2021-27 operational program (Ref. ED431G 2023/01). We thank the support from Ministry for Digital Transformation and Civil Service and Next-GenerationEU/RRF (TSI-100925-2023-1). SMR has received funding from Xunta de Galicia (grant ED481A 2023/008).es_ES
dc.description.sponsorshipXunta de Galicia; ED431C 2022/44es_ES
dc.description.sponsorshipXunta de Galicia; ED431G 2023/01es_ES
dc.description.sponsorshipXunta de Galicia; ED481A 2023/008es_ES
dc.identifier.citationMosqueira-Rey, E., Magaz-Romero, S., & Moret-Bonillo, V. Unified Framework for Implementing Inaccurate Knowledge in Quantum Symbolic Artificial Intelligence Models. In Proceedings of the 17th International Conference on Agents and Artificial Intelligence (ICAART 2025) - Volume 1, pages 839-846. DOI: 10.5220/001340020003890.es_ES
dc.identifier.doi10.5220/001340020003890
dc.identifier.isbn978-989-758-737-5
dc.identifier.issn2184-433X
dc.identifier.urihttp://hdl.handle.net/2183/42040
dc.language.isoenges_ES
dc.publisherSCITEPRESS - Science and Technology Publicationses_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/951821es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023/PID2023-147422OB-I00/ES/ALGORITMOS DE APRENDIZAJE AUTOMATICO DE NUEVA GENERACION PARA EL ANALISIS DE REGISTROS MEDICOS DEL SUEÑOes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/MTDPF//TSI-100925-2023-1/ES/CÁTEDRA UDC-INDITEX DE IA EN ALGORITMOS VERDESes_ES
dc.relation.urihttps://doi.org/10.5220/0013400200003890es_ES
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacionales_ES
dc.rightsCopyright © 2025 by SCITEPRESS – Science and Technology Publications, Lda.es_ES
dc.rights.accessRightsopen accesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectQuantum symbolic AIes_ES
dc.subjectQuantum inaccurate knowledgees_ES
dc.subjectCertainty factorses_ES
dc.subjectBayesian networkses_ES
dc.subjectFuzzy modelses_ES
dc.titleUnified framework for implementing inaccurate knowledge in quantum symbolic artificial intelligence modelses_ES
dc.typeconference outputes_ES
dc.type.hasVersionVoRes_ES
dspace.entity.typePublication
relation.isAuthorOfPublication770502c4-505f-4b52-80e6-22359cb07b44
relation.isAuthorOfPublication8e3bfc85-ea7d-45cc-b9cf-54b878ca8b97
relation.isAuthorOfPublication34c5d35a-6252-444a-b6ce-d97dfe8f01eb
relation.isAuthorOfPublication.latestForDiscovery770502c4-505f-4b52-80e6-22359cb07b44

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