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A common framework for learning causality

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A common framework for learning causality

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dc.contributor.author Onaindia De La Rivaherrera, Eva es_ES
dc.contributor.author Aineto, Diego es_ES
dc.contributor.author Jiménez-Celorrio, Sergio es_ES
dc.date.accessioned 2019-10-02T06:16:25Z
dc.date.available 2019-10-02T06:16:25Z
dc.date.issued 2018 es_ES
dc.identifier.issn 2192-6352 es_ES
dc.identifier.uri http://hdl.handle.net/10251/126938
dc.description.abstract [EN] Causality is a fundamental part of reasoning to model the physics of an application domain, to understand the behaviour of an agent or to identify the relationship between two entities. Causality occurs when an action is taken and may also occur when two happenings come undeniably together. The study of causal inference aims at uncovering causal dependencies among observed data and to come up with automated methods to find such dependencies. While there exist a broad range of principles and approaches involved in causal inference, in this position paper we argue that it is possible to unify different causality views under a common framework of symbolic learning. es_ES
dc.description.sponsorship This work is supported by the Spanish MINECO project TIN2017-88476-C2-1-R. Diego Aineto is partially supported by the FPU16/03184 and Sergio Jimenez by the RYC15/18009, both programs funded by the Spanish government. es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Progress in Artificial Intelligence es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Causal inference es_ES
dc.subject Action models es_ES
dc.subject Behaviour prediction es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.subject.classification CIENCIAS DE LA COMPUTACION E INTELIGENCIA ARTIFICIAL es_ES
dc.title A common framework for learning causality es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s13748-018-0151-y es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2017-88476-C2-1-R/ES/RECONOCIMIENTO DE ACTIVIDADES Y PLANIFICACION AUTOMATICA PARA EL DISEÑO DE ASISTENTES INTELIGENTES/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MECD//FPU16%2F03184/ES/FPU16%2F03184/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//RYC-2015-18009/ES/RYC-2015-18009/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.description.bibliographicCitation Onaindia De La Rivaherrera, E.; Aineto, D.; Jiménez-Celorrio, S. (2018). A common framework for learning causality. Progress in Artificial Intelligence. 7(4):351-357. https://doi.org/10.1007/s13748-018-0151-y es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s13748-018-0151-y es_ES
dc.description.upvformatpinicio 351 es_ES
dc.description.upvformatpfin 357 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 7 es_ES
dc.description.issue 4 es_ES
dc.relation.pasarela S\370074 es_ES
dc.contributor.funder Ministerio de Educación, Cultura y Deporte es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
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