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dc.contributor.authorGil Ruiz, Jesús
dc.contributor.authorMartinez Torres, Javier
dc.contributor.authorGonzález-Crespo, Rubén
dc.date2021
dc.date.accessioned2022-01-14T11:37:10Z
dc.date.available2022-01-14T11:37:10Z
dc.identifier.issn1989-1660
dc.identifier.urihttps://reunir.unir.net/handle/123456789/12309
dc.description.abstractThe field of artificial intelligence is currently experiencing relentless growth, with innumerable models emerging in the research and development phases across various fields, including science, finance, and engineering. In this work, the authors review a large number of learning techniques aimed at project management. The analysis is largely focused on hybrid systems, which present computational models of blended learning techniques. At present, these models are at a very early stage and major efforts in terms of development is required within the scientific community. In addition, we provide a classification of all the areas within project management and the learning techniques that are used in each, presenting a brief study of the different artificial intelligence techniques used today and the areas of project management in which agents are being applied. This work should serve as a starting point for researchers who wish to work in the exciting world of artificial intelligence in relation to project leadership and management.es_ES
dc.language.isoenges_ES
dc.publisherInternational Journal of Interactive Multimedia and Artificial Intelligencees_ES
dc.relation.ispartofseries;vol. 6, nº 6
dc.relation.urihttps://www.ijimai.org/journal/bibcite/reference/2868es_ES
dc.rightsopenAccesses_ES
dc.subjectartificial intelligencees_ES
dc.subjectdecision support systemses_ES
dc.subjectevolutionary diffuse hybrid neuronal networkes_ES
dc.subjectproject managementes_ES
dc.subjectproject successes_ES
dc.subjectcriticales_ES
dc.subjectWOS(2)es_ES
dc.subjectScopuses_ES
dc.titleThe Application of Artificial Intelligence in Project Management Research: A Reviewes_ES
dc.typearticlees_ES
reunir.tag~ARIes_ES
dc.identifier.doihttps://doi.org/10.9781/ijimai.2020.12.003


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