EGAR: Environment Generator for Agent-Based Research

UDC.coleccionInvestigaciónes_ES
UDC.conferenceTitlePAAMS 2024 - Advances in Practical Applications of Agents, Multi-Agent Systems, and Digital Twinses_ES
UDC.departamentoCiencias da Computación e Tecnoloxías da Informaciónes_ES
UDC.endPage227es_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.startPage217es_ES
UDC.volumeLecture Notes in Computer Science (LNAI,volume 15157)es_ES
dc.contributor.authorRodríguez-Arias, Alejandro
dc.contributor.authorSánchez-Maroño, Noelia
dc.contributor.authorGuijarro-Berdiñas, Bertha
dc.date.accessioned2025-05-22T15:39:28Z
dc.date.embargoEndDate2025-12-01es_ES
dc.date.embargoLift2025-12-01
dc.date.issued2025
dc.descriptionPresented in the following conference: PAAMS 2024: 22nd International Conference on Advances in Practical Applications of Agents, Multi-Agent Systems, and Digital Twins, Salamanca, Spain, June 26–28, 2024.es_ES
dc.descriptionThis version of the conference paper has been accepted for publication, after peer review; it is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/978-3-031-70415-4_19es_ES
dc.description.abstract[Abstract]: Agent-based modeling (ABM) allows the representation of real-world phenomena using agents, a virtual environment, and interactions between them. It has become one of the most important tools for studying complex phenomena in social sciences. The correct placement of agents, locations, structures, and objects in the virtual environment, e.g. a city, is important in the development of an ABM in order to increase the realism and accuracy of the simulations. Unity is a game engine that allows the development of ABM with the advantage of greatly optimizing the management of resources compared to other alternatives, making it possible to reduce simulation time or handle more complex models. In this work, a tool is proposed that allows the generation of a more precise virtual environment for the development of ABM in Unity. The tool developed facilitates the visualization and distribution of agents and the creation of networks between them. It provides a way to explore and understand the social and spatial processes taking place in the simulated environment. By combining ABM, GIS data, and visualization tools in virtual environments, researchers have a robust framework for tackling complex modeling problems. For example, in social sciences, this integrated approach allows the exploration of hypothetical scenarios, the evaluation of intervention strategies, and a better understanding of networks of influence. Two real-world problems, the spread of a virus and the implementation of a new urban plan based on superblocks, are presented to illustrate this tool.es_ES
dc.description.sponsorshipXunta de Galicia, together with the ERDF funds of the European Union, through its grants to research groups (Grant no. ED431C 2022/44), the Spanish National Plan for Scientific and Technical Research and Innovation (PID2019-109238GB-C22), and CITIC, as a Research Center of the University System of Galicia (Grant no. ED431G 2019/01).es_ES
dc.description.sponsorshipXunta de Galicia; ED431C 2022/44es_ES
dc.description.sponsorshipXunta de Galicia; ED431G 2019/01es_ES
dc.identifier.citationRodríguez-Arias, A., Sánchez-Maroño, N., Guijarro-Berdiñas, B. (2025). EGAR: Environment Generator for Agent-Based Research. In: Mathieu, P., De la Prieta, F. (eds) Advances in Practical Applications of Agents, Multi-Agent Systems, and Digital Twins: The PAAMS Collection. PAAMS 2024. Lecture Notes in Computer Science(), vol 15157. Springer, Cham. https://doi.org/10.1007/978-3-031-70415-4_19es_ES
dc.identifier.doi10.1007/978-3-031-70415-4_19
dc.identifier.isbn9783031704147
dc.identifier.issn1611-3349
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/2183/42067
dc.language.isoenges_ES
dc.publisherSpringeres_ES
dc.relation.ispartofseriesLecture Notes in Computer Science (LNCS), including its subseries Lecture Notes in Artificial Intelligence (LNAI) and Lecture Notes in Bioinformatics (LNBI)es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109238GB-C22/ES/APRENDIZAJE AUTOMATICO ESCALABLE Y EXPLICABLEes_ES
dc.relation.urihttps://doi.org/10.1007/978-3-031-70415-4_19es_ES
dc.rights© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG. This version is subject to Springer Nature’s AM terms of use - https://www.springernature.com/gp/open-research/policies/accepted-manuscript-termses_ES
dc.rights.accessRightsopen accesses_ES
dc.subjectAgent-based modelinges_ES
dc.subjectCensus tractses_ES
dc.subjectComputational social sciencees_ES
dc.subjectUnityes_ES
dc.subjectVirtual environmentes_ES
dc.titleEGAR: Environment Generator for Agent-Based Researches_ES
dc.typeconference outputes_ES
dc.type.hasVersionAMes_ES
dspace.entity.typePublication
relation.isAuthorOfPublicationaef56194-e82a-446f-9d96-8acc50f51723
relation.isAuthorOfPublicationd839396d-454e-4ccd-9322-d3e89a876865
relation.isAuthorOfPublication.latestForDiscoveryaef56194-e82a-446f-9d96-8acc50f51723

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