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Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

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Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering

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dc.contributor.author Herrera, Manuel es_ES
dc.contributor.author Pérez-Hernández, Marco es_ES
dc.contributor.author Parlikad, Ajith Kumar es_ES
dc.contributor.author Izquierdo Sebastián, Joaquín es_ES
dc.date.accessioned 2021-03-01T08:08:10Z
dc.date.available 2021-03-01T08:08:10Z
dc.date.issued 2020-03 es_ES
dc.identifier.uri http://hdl.handle.net/10251/162561
dc.description.abstract [EN] Systems engineering is an ubiquitous discipline of Engineering overlapping industrial, chemical, mechanical, manufacturing, control, software, electrical, and civil engineering. It provides tools for dealing with the complexity and dynamics related to the optimisation of physical, natural, and virtual systems management. This paper presents a review of how multi-agent systems and complex networks theory are brought together to address systems engineering and management problems. The review also encompasses current and future research directions both for theoretical fundamentals and applications in the industry. This is made by considering trends such as mesoscale, multiscale, and multilayer networks along with the state-of-art analysis on network dynamics and intelligent networks. Critical and smart infrastructure, manufacturing processes, and supply chain networks are instances of research topics for which this literature review is highly relevant. es_ES
dc.description.sponsorship This research was funded by the EPSRC and BT Prosperity Partnership project: Next Generation Converged Digital Infrastructure, grant number EP/R004935/1. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Processes es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Systems engineering es_ES
dc.subject Complex networks es_ES
dc.subject Multi-agent systems es_ES
dc.subject Optimisation es_ES
dc.subject Processes systems engineering es_ES
dc.subject Agent-based control es_ES
dc.subject.classification MATEMATICA APLICADA es_ES
dc.title Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/pr8030312 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UKRI//EP%2FR004935%2F1/GB/Next Generation Converged Digital infrastructure (NG-CDI)/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Matemática Multidisciplinar - Institut Universitari de Matemàtica Multidisciplinària es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada es_ES
dc.description.bibliographicCitation Herrera, M.; Pérez-Hernández, M.; Parlikad, AK.; Izquierdo Sebastián, J. (2020). Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering. Processes. 8(3):1-29. https://doi.org/10.3390/pr8030312 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/pr8030312 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 29 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 8 es_ES
dc.description.issue 3 es_ES
dc.identifier.eissn 2227-9717 es_ES
dc.relation.pasarela S\407893 es_ES
dc.contributor.funder Engineering and Physical Sciences Research Council, Reino Unido es_ES
dc.contributor.funder UK Research and Innovation es_ES
dc.description.references Strogatz, S. H. (2001). Exploring complex networks. Nature, 410(6825), 268-276. doi:10.1038/35065725 es_ES
dc.description.references Winkler, J., Dueñas-Osorio, L., Stein, R., & Subramanian, D. (2011). Interface Network Models for Complex Urban Infrastructure Systems. Journal of Infrastructure Systems, 17(4), 138-150. doi:10.1061/(asce)is.1943-555x.0000068 es_ES
dc.description.references Nekovee, M., Moreno, Y., Bianconi, G., & Marsili, M. (2007). Theory of rumour spreading in complex social networks. Physica A: Statistical Mechanics and its Applications, 374(1), 457-470. doi:10.1016/j.physa.2006.07.017 es_ES
dc.description.references Wong, A. S. Y., & Huck, W. T. S. (2017). Grip on complexity in chemical reaction networks. Beilstein Journal of Organic Chemistry, 13, 1486-1497. doi:10.3762/bjoc.13.147 es_ES
dc.description.references Gosak, M., Markovič, R., Dolenšek, J., Slak Rupnik, M., Marhl, M., Stožer, A., & Perc, M. (2018). Network science of biological systems at different scales: A review. Physics of Life Reviews, 24, 118-135. doi:10.1016/j.plrev.2017.11.003 es_ES
dc.description.references Van Steen, M., & Tanenbaum, A. S. (2016). A brief introduction to distributed systems. Computing, 98(10), 967-1009. doi:10.1007/s00607-016-0508-7 es_ES
dc.description.references Yang, T., Yi, X., Wu, J., Yuan, Y., Wu, D., Meng, Z., … Johansson, K. H. (2019). A survey of distributed optimization. Annual Reviews in Control, 47, 278-305. doi:10.1016/j.arcontrol.2019.05.006 es_ES
dc.description.references Charyyev, B., & Gunes, M. H. (2019). Complex network of United States migration. Computational Social Networks, 6(1). doi:10.1186/s40649-019-0061-6 es_ES
dc.description.references Del Vicario, M., Bessi, A., Zollo, F., Petroni, F., Scala, A., Caldarelli, G., … Quattrociocchi, W. (2016). The spreading of misinformation online. Proceedings of the National Academy of Sciences, 113(3), 554-559. doi:10.1073/pnas.1517441113 es_ES
dc.description.references Manuel, P. (2010). Computational Aspects of Carbon and Boron Nanotubes. Molecules, 15(12), 8709-8722. doi:10.3390/molecules15128709 es_ES
dc.description.references Hinkelmann, F., Murrugarra, D., Jarrah, A. S., & Laubenbacher, R. (2010). A Mathematical Framework for Agent Based Models of Complex Biological Networks. Bulletin of Mathematical Biology, 73(7), 1583-1602. doi:10.1007/s11538-010-9582-8 es_ES
dc.description.references Zhao, J., Yu, H., Luo, J., Cao, Z. W., & Li, Y. (2006). Complex networks theory for analyzing metabolic networks. Chinese Science Bulletin, 51(13), 1529-1537. doi:10.1007/s11434-006-2015-2 es_ES
dc.description.references Borer, B., Ataman, M., Hatzimanikatis, V., & Or, D. (2019). Modeling metabolic networks of individual bacterial agents in heterogeneous and dynamic soil habitats (IndiMeSH). PLOS Computational Biology, 15(6), e1007127. doi:10.1371/journal.pcbi.1007127 es_ES
dc.description.references Morstyn, T., Hredzak, B., & Agelidis, V. G. (2018). Network Topology Independent Multi-Agent Dynamic Optimal Power Flow for Microgrids With Distributed Energy Storage Systems. IEEE Transactions on Smart Grid, 9(4), 3419-3429. doi:10.1109/tsg.2016.2631600 es_ES
dc.description.references Kiesling, E., Günther, M., Stummer, C., & Wakolbinger, L. M. (2011). Agent-based simulation of innovation diffusion: a review. Central European Journal of Operations Research, 20(2), 183-230. doi:10.1007/s10100-011-0210-y es_ES
dc.description.references Nair, A. S., Hossen, T., Campion, M., Selvaraj, D. F., Goveas, N., Kaabouch, N., & Ranganathan, P. (2018). Multi-Agent Systems for Resource Allocation and Scheduling in a Smart Grid. Technology and Economics of Smart Grids and Sustainable Energy, 3(1). doi:10.1007/s40866-018-0052-y es_ES
dc.description.references Brintrup, A., Wang, Y., & Tiwari, A. (2017). Supply Networks as Complex Systems: A Network-Science-Based Characterization. IEEE Systems Journal, 11(4), 2170-2181. doi:10.1109/jsyst.2015.2425137 es_ES
dc.description.references Guimerà, R., & Nunes Amaral, L. A. (2005). Functional cartography of complex metabolic networks. Nature, 433(7028), 895-900. doi:10.1038/nature03288 es_ES
dc.description.references Zio, E. (2007). From complexity science to reliability efficiency: a new way of looking at complex network systems and critical infrastructures. International Journal of Critical Infrastructures, 3(3/4), 488. doi:10.1504/ijcis.2007.014122 es_ES
dc.description.references Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440-442. doi:10.1038/30918 es_ES
dc.description.references Barabási, A.-L. (2009). Scale-Free Networks: A Decade and Beyond. Science, 325(5939), 412-413. doi:10.1126/science.1173299 es_ES
dc.description.references Viana, M. P., Strano, E., Bordin, P., & Barthelemy, M. (2013). The simplicity of planar networks. Scientific Reports, 3(1). doi:10.1038/srep03495 es_ES
dc.description.references Boeing, G. (2018). Planarity and street network representation in urban form analysis. Environment and Planning B: Urban Analytics and City Science, 47(5), 855-869. doi:10.1177/2399808318802941 es_ES
dc.description.references Diet, A., & Barthelemy, M. (2018). Towards a classification of planar maps. Physical Review E, 98(6). doi:10.1103/physreve.98.062304 es_ES
dc.description.references Strano, E., Nicosia, V., Latora, V., Porta, S., & Barthélemy, M. (2012). Elementary processes governing the evolution of road networks. Scientific Reports, 2(1). doi:10.1038/srep00296 es_ES
dc.description.references Giudicianni, C., Di Nardo, A., Di Natale, M., Greco, R., Santonastaso, G., & Scala, A. (2018). Topological Taxonomy of Water Distribution Networks. Water, 10(4), 444. doi:10.3390/w10040444 es_ES
dc.description.references Girvan, M., & Newman, M. E. J. (2002). Community structure in social and biological networks. Proceedings of the National Academy of Sciences, 99(12), 7821-7826. doi:10.1073/pnas.122653799 es_ES
dc.description.references Rieckmann, J. C., Geiger, R., Hornburg, D., Wolf, T., Kveler, K., Jarrossay, D., … Meissner, F. (2017). Social network architecture of human immune cells unveiled by quantitative proteomics. Nature Immunology, 18(5), 583-593. doi:10.1038/ni.3693 es_ES
dc.description.references Kurvers, R. H. J. M., Krause, J., Croft, D. P., Wilson, A. D. M., & Wolf, M. (2014). The evolutionary and ecological consequences of animal social networks: emerging issues. Trends in Ecology & Evolution, 29(6), 326-335. doi:10.1016/j.tree.2014.04.002 es_ES
dc.description.references Brentan, B., Campbell, E., Goulart, T., Manzi, D., Meirelles, G., Herrera, M., … Luvizotto, E. (2018). Social Network Community Detection and Hybrid Optimization for Dividing Water Supply into District Metered Areas. Journal of Water Resources Planning and Management, 144(5), 04018020. doi:10.1061/(asce)wr.1943-5452.0000924 es_ES
dc.description.references Salvador Palau, A., Liang, Z., Lütgehetmann, D., & Parlikad, A. K. (2019). Collaborative prognostics in Social Asset Networks. Future Generation Computer Systems, 92, 987-995. doi:10.1016/j.future.2018.02.011 es_ES
dc.description.references Lee, S. H., Cucuringu, M., & Porter, M. A. (2014). Density-based and transport-based core-periphery structures in networks. Physical Review E, 89(3). doi:10.1103/physreve.89.032810 es_ES
dc.description.references Verma, T., Russmann, F., Araújo, N. A. M., Nagler, J., & Herrmann, H. J. (2016). Emergence of core–peripheries in networks. Nature Communications, 7(1). doi:10.1038/ncomms10441 es_ES
dc.description.references Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32(3), 245-251. doi:10.1016/j.socnet.2010.03.006 es_ES
dc.description.references Freeman, L. C. (1977). A Set of Measures of Centrality Based on Betweenness. Sociometry, 40(1), 35. doi:10.2307/3033543 es_ES
dc.description.references Wuchty, S., & Stadler, P. F. (2003). Centers of complex networks. Journal of Theoretical Biology, 223(1), 45-53. doi:10.1016/s0022-5193(03)00071-7 es_ES
dc.description.references Bonacich, P. (1972). Factoring and weighting approaches to status scores and clique identification. The Journal of Mathematical Sociology, 2(1), 113-120. doi:10.1080/0022250x.1972.9989806 es_ES
dc.description.references Brin, S., & Page, L. (2012). Reprint of: The anatomy of a large-scale hypertextual web search engine. Computer Networks, 56(18), 3825-3833. doi:10.1016/j.comnet.2012.10.007 es_ES
dc.description.references Katz, L. (1953). A new status index derived from sociometric analysis. Psychometrika, 18(1), 39-43. doi:10.1007/bf02289026 es_ES
dc.description.references Serrano, M. Á., & Boguñá, M. (2006). Clustering in complex networks. I. General formalism. Physical Review E, 74(5). doi:10.1103/physreve.74.056114 es_ES
dc.description.references Suchecki, K., Eguíluz, V. M., & San Miguel, M. (2005). Voter model dynamics in complex networks: Role of dimensionality, disorder, and degree distribution. Physical Review E, 72(3). doi:10.1103/physreve.72.036132 es_ES
dc.description.references Noldus, R., & Van Mieghem, P. (2015). Assortativity in complex networks. Journal of Complex Networks, 3(4), 507-542. doi:10.1093/comnet/cnv005 es_ES
dc.description.references Albert, R., & Barabási, A.-L. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74(1), 47-97. doi:10.1103/revmodphys.74.47 es_ES
dc.description.references Gao, J., Barzel, B., & Barabási, A.-L. (2016). Universal resilience patterns in complex networks. Nature, 530(7590), 307-312. doi:10.1038/nature16948 es_ES
dc.description.references Li, D., Zhang, Q., Zio, E., Havlin, S., & Kang, R. (2015). Network reliability analysis based on percolation theory. Reliability Engineering & System Safety, 142, 556-562. doi:10.1016/j.ress.2015.05.021 es_ES
dc.description.references Gao, J., Liu, X., Li, D., & Havlin, S. (2015). Recent Progress on the Resilience of Complex Networks. Energies, 8(10), 12187-12210. doi:10.3390/en81012187 es_ES
dc.description.references Chen, X. G. (2017). A novel reliability estimation method of complex network based on Monte Carlo. Cluster Computing, 20(2), 1063-1073. doi:10.1007/s10586-017-0826-3 es_ES
dc.description.references Kroese, D. P., Brereton, T., Taimre, T., & Botev, Z. I. (2014). Why the Monte Carlo method is so important today. WIREs Computational Statistics, 6(6), 386-392. doi:10.1002/wics.1314 es_ES
dc.description.references Newman, M. E. J., & Ziff, R. M. (2001). Fast Monte Carlo algorithm for site or bond percolation. Physical Review E, 64(1). doi:10.1103/physreve.64.016706 es_ES
dc.description.references Li, D., Fu, B., Wang, Y., Lu, G., Berezin, Y., Stanley, H. E., & Havlin, S. (2014). Percolation transition in dynamical traffic network with evolving critical bottlenecks. Proceedings of the National Academy of Sciences, 112(3), 669-672. doi:10.1073/pnas.1419185112 es_ES
dc.description.references Carvalho, R., Buzna, L., Bono, F., Masera, M., Arrowsmith, D. K., & Helbing, D. (2014). Resilience of Natural Gas Networks during Conflicts, Crises and Disruptions. PLoS ONE, 9(3), e90265. doi:10.1371/journal.pone.0090265 es_ES
dc.description.references Torres, J. M., Duenas-Osorio, L., Li, Q., & Yazdani, A. (2017). Exploring Topological Effects on Water Distribution System Performance Using Graph Theory and Statistical Models. Journal of Water Resources Planning and Management, 143(1), 04016068. doi:10.1061/(asce)wr.1943-5452.0000709 es_ES
dc.description.references Chen, Y., Li, Y., Li, W., Wu, X., Cai, Y., Cao, Y., & Rehtanz, C. (2018). Cascading Failure Analysis of Cyber Physical Power System With Multiple Interdependency and Control Threshold. IEEE Access, 6, 39353-39362. doi:10.1109/access.2018.2855441 es_ES
dc.description.references Hui, K.-P. (2007). Monte Carlo Network Reliability Ranking Estimation. IEEE Transactions on Reliability, 56(1), 50-57. doi:10.1109/tr.2006.890898 es_ES
dc.description.references Piraveenan, M., Prokopenko, M., & Hossain, L. (2013). Percolation Centrality: Quantifying Graph-Theoretic Impact of Nodes during Percolation in Networks. PLoS ONE, 8(1), e53095. doi:10.1371/journal.pone.0053095 es_ES
dc.description.references Liao, H., Mariani, M. S., Medo, M., Zhang, Y.-C., & Zhou, M.-Y. (2017). Ranking in evolving complex networks. Physics Reports, 689, 1-54. doi:10.1016/j.physrep.2017.05.001 es_ES
dc.description.references Morone, F., & Makse, H. A. (2015). Influence maximization in complex networks through optimal percolation. Nature, 524(7563), 65-68. doi:10.1038/nature14604 es_ES
dc.description.references Lü, L., Chen, D., Ren, X.-L., Zhang, Q.-M., Zhang, Y.-C., & Zhou, T. (2016). Vital nodes identification in complex networks. Physics Reports, 650, 1-63. doi:10.1016/j.physrep.2016.06.007 es_ES
dc.description.references Jalili, M., & Yu, X. (2016). Enhancement of Synchronizability in Networks with Community Structure through Adding Efficient Inter-Community Links. IEEE Transactions on Network Science and Engineering, 3(2), 106-116. doi:10.1109/tnse.2016.2566615 es_ES
dc.description.references Jalili, M., & Perc, M. (2017). Information cascades in complex networks. Journal of Complex Networks. doi:10.1093/comnet/cnx019 es_ES
dc.description.references Chen, D., Lü, L., Shang, M.-S., Zhang, Y.-C., & Zhou, T. (2012). Identifying influential nodes in complex networks. Physica A: Statistical Mechanics and its Applications, 391(4), 1777-1787. doi:10.1016/j.physa.2011.09.017 es_ES
dc.description.references Lawyer, G. (2015). Understanding the influence of all nodes in a network. Scientific Reports, 5(1). doi:10.1038/srep08665 es_ES
dc.description.references Zhang, Z.-K., Liu, C., Zhan, X.-X., Lu, X., Zhang, C.-X., & Zhang, Y.-C. (2016). Dynamics of information diffusion and its applications on complex networks. Physics Reports, 651, 1-34. doi:10.1016/j.physrep.2016.07.002 es_ES
dc.description.references Dai, X., Hu, M., Tian, W., Xie, D., & Hu, B. (2016). Application of Epidemiology Model on Complex Networks in Propagation Dynamics of Airspace Congestion. PLOS ONE, 11(6), e0157945. doi:10.1371/journal.pone.0157945 es_ES
dc.description.references Pastor-Satorras, R., Castellano, C., Van Mieghem, P., & Vespignani, A. (2015). Epidemic processes in complex networks. Reviews of Modern Physics, 87(3), 925-979. doi:10.1103/revmodphys.87.925 es_ES
dc.description.references Bardet, J.-P., & Little, R. (2014). Epidemiology of urban water distribution systems. Water Resources Research, 50(8), 6447-6465. doi:10.1002/2013wr015017 es_ES
dc.description.references Ding, L., Li, K., Zhou, Y., & Love, P. E. D. (2017). An IFC-inspection process model for infrastructure projects: Enabling real-time quality monitoring and control. Automation in Construction, 84, 96-110. doi:10.1016/j.autcon.2017.08.029 es_ES
dc.description.references Kim, H., & Anderson, R. (2012). Temporal node centrality in complex networks. Physical Review E, 85(2). doi:10.1103/physreve.85.026107 es_ES
dc.description.references Braha, D., & Bar-Yam, Y. (2006). From centrality to temporary fame: Dynamic centrality in complex networks. Complexity, 12(2), 59-63. doi:10.1002/cplx.20156 es_ES
dc.description.references Shekhtman, L. M., Danziger, M. M., & Havlin, S. (2016). Recent advances on failure and recovery in networks of networks. Chaos, Solitons & Fractals, 90, 28-36. doi:10.1016/j.chaos.2016.02.002 es_ES
dc.description.references Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J. P., Moreno, Y., & Porter, M. A. (2014). Multilayer networks. Journal of Complex Networks, 2(3), 203-271. doi:10.1093/comnet/cnu016 es_ES
dc.description.references De Domenico, M., Solé-Ribalta, A., Cozzo, E., Kivelä, M., Moreno, Y., Porter, M. A., … Arenas, A. (2013). Mathematical Formulation of Multilayer Networks. Physical Review X, 3(4). doi:10.1103/physrevx.3.041022 es_ES
dc.description.references Rahmede, C., Iacovacci, J., Arenas, A., & Bianconi, G. (2017). Centralities of nodes and influences of layers in large multiplex networks. Journal of Complex Networks, 6(5), 733-752. doi:10.1093/comnet/cnx050 es_ES
dc.description.references Gómez, S., Díaz-Guilera, A., Gómez-Gardeñes, J., Pérez-Vicente, C. J., Moreno, Y., & Arenas, A. (2013). Diffusion Dynamics on Multiplex Networks. Physical Review Letters, 110(2). doi:10.1103/physrevlett.110.028701 es_ES
dc.description.references Zhao, D., Li, L., Peng, H., Luo, Q., & Yang, Y. (2014). Multiple routes transmitted epidemics on multiplex networks. Physics Letters A, 378(10), 770-776. doi:10.1016/j.physleta.2014.01.014 es_ES
dc.description.references De Domenico, M., Granell, C., Porter, M. A., & Arenas, A. (2016). The physics of spreading processes in multilayer networks. Nature Physics, 12(10), 901-906. doi:10.1038/nphys3865 es_ES
dc.description.references Cellai, D., López, E., Zhou, J., Gleeson, J. P., & Bianconi, G. (2013). Percolation in multiplex networks with overlap. Physical Review E, 88(5). doi:10.1103/physreve.88.052811 es_ES
dc.description.references Osat, S., Faqeeh, A., & Radicchi, F. (2017). Optimal percolation on multiplex networks. Nature Communications, 8(1). doi:10.1038/s41467-017-01442-2 es_ES
dc.description.references He, W., Chen, G., Han, Q.-L., Du, W., Cao, J., & Qian, F. (2017). Multiagent Systems on Multilayer Networks: Synchronization Analysis and Network Design. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(7), 1655-1667. doi:10.1109/tsmc.2017.2659759 es_ES
dc.description.references Milanovic, J. V., & Zhu, W. (2018). Modeling of Interconnected Critical Infrastructure Systems Using Complex Network Theory. IEEE Transactions on Smart Grid, 9(5), 4637-4648. doi:10.1109/tsg.2017.2665646 es_ES
dc.description.references Nwana, H. S. (1996). Software agents: an overview. The Knowledge Engineering Review, 11(3), 205-244. doi:10.1017/s026988890000789x es_ES
dc.description.references Macal, C. M., & North, M. J. (2009). Agent-based modeling and simulation. Proceedings of the 2009 Winter Simulation Conference (WSC). doi:10.1109/wsc.2009.5429318 es_ES
dc.description.references Macal, C. M., & North, M. J. (2010). Tutorial on agent-based modelling and simulation. Journal of Simulation, 4(3), 151-162. doi:10.1057/jos.2010.3 es_ES
dc.description.references Bonabeau, E. (2002). Agent-based modeling: Methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99(Supplement 3), 7280-7287. doi:10.1073/pnas.082080899 es_ES
dc.description.references Belsare, A. V., & Gompper, M. E. (2015). A model-based approach for investigation and mitigation of disease spillover risks to wildlife: Dogs, foxes and canine distemper in central India. Ecological Modelling, 296, 102-112. doi:10.1016/j.ecolmodel.2014.10.031 es_ES
dc.description.references Raberto, M., Cincotti, S., Focardi, S. M., & Marchesi, M. (2001). Agent-based simulation of a financial market. Physica A: Statistical Mechanics and its Applications, 299(1-2), 319-327. doi:10.1016/s0378-4371(01)00312-0 es_ES
dc.description.references Barbosa, J., & Leitao, P. (2011). Simulation of multi-agent manufacturing systems using Agent-Based Modelling platforms. 2011 9th IEEE International Conference on Industrial Informatics. doi:10.1109/indin.2011.6034926 es_ES
dc.description.references Wooldridge, M., & Jennings, N. R. (1995). Intelligent agents: theory and practice. The Knowledge Engineering Review, 10(2), 115-152. doi:10.1017/s0269888900008122 es_ES
dc.description.references Franklin, S., & Graesser, A. (1997). Is It an agent, or just a program?: A taxonomy for autonomous agents. Lecture Notes in Computer Science, 21-35. doi:10.1007/bfb0013570 es_ES
dc.description.references HEXMOOR, H. (2002). A model of absolute autonomy and power: Toward group effects. Connection Science, 14(4), 323-333. doi:10.1080/0954009021000068727 es_ES
dc.description.references Hexmoor, H., Castelfranchi, C., & Falcone, R. (Eds.). (2003). Agent Autonomy. Multiagent Systems, Artificial Societies, and Simulated Organizations. doi:10.1007/978-1-4419-9198-0 es_ES
dc.description.references Brewka, G. (1996). Artificial intelligence—a modern approach by Stuart Russell and Peter Norvig, Prentice Hall. Series in Artificial Intelligence, Englewood Cliffs, NJ. The Knowledge Engineering Review, 11(1), 78-79. doi:10.1017/s0269888900007724 es_ES
dc.description.references Agent based Modelling and Simulation using State Machines. (2012). Proceedings of the 2nd International Conference on Simulation and Modeling Methodologies, Technologies and Applications. doi:10.5220/0004164802700279 es_ES
dc.description.references Miao, C. Y., Goh, A., Miao, Y., & Yang, Z. H. (2002). Agent that models, reasons and makes decisions. Knowledge-Based Systems, 15(3), 203-211. doi:10.1016/s0950-7051(01)00157-5 es_ES
dc.description.references Dibley, M., Li, H., Rezgui, Y., & Miles, J. (2015). AN INTEGRATED FRAMEWORK UTILISING SOFTWARE AGENT REASONING AND ONTOLOGY MODELS FOR SENSOR BASED BUILDING MONITORING. Journal of Civil Engineering and Management, 21(3), 356-375. doi:10.3846/13923730.2014.890645 es_ES
dc.description.references González, E. J., Hamilton, A. F., Moreno, L., Marichal, R. L., & Muñoz, V. (2006). Software experience when using ontologies in a multi-agent system for automated planning and scheduling. Software: Practice and Experience, 36(7), 667-688. doi:10.1002/spe.711 es_ES
dc.description.references Ward, J. A., Evans, A. J., & Malleson, N. S. (2016). Dynamic calibration of agent-based models using data assimilation. Royal Society Open Science, 3(4), 150703. doi:10.1098/rsos.150703 es_ES
dc.description.references Wooldridge, M., & Jennings, N. R. (1995). Agent theories, architectures, and languages: A survey. Intelligent Agents, 1-39. doi:10.1007/3-540-58855-8_1 es_ES
dc.description.references Consoli, A., Tweedale, J., & Jain, L. (s. f.). The Link between Agent Coordination and Cooperation. Intelligent Information Processing III, 11-19. doi:10.1007/978-0-387-44641-7_2 es_ES
dc.description.references FIPA ACL Message Structure Specificationhttp://www.fipa.org/specs/fipa00061/SC00061G.html es_ES
dc.description.references Kibble, R. (2006). Speech acts, commitment and multi-agent communication. Computational & Mathematical Organization Theory, 12(2-3), 127-145. doi:10.1007/s10588-006-9540-z es_ES
dc.description.references Hadeli, Valckenaers, P., Kollingbaum, M., & Van Brussel, H. (2004). Multi-agent coordination and control using stigmergy. Computers in Industry, 53(1), 75-96. doi:10.1016/s0166-3615(03)00123-4 es_ES
dc.description.references Olfati-Saber, R., Fax, J. A., & Murray, R. M. (2007). Consensus and Cooperation in Networked Multi-Agent Systems. Proceedings of the IEEE, 95(1), 215-233. doi:10.1109/jproc.2006.887293 es_ES
dc.description.references Gulzar, M. M., Rizvi, S. T. H., Javed, M. Y., Munir, U., & Asif, H. (2018). Multi-Agent Cooperative Control Consensus: A Comparative Review. Electronics, 7(2), 22. doi:10.3390/electronics7020022 es_ES
dc.description.references Zambonelli, F., Omicini, A., Anzengruber, B., Castelli, G., De Angelis, F. L., Serugendo, G. D. M., … Ye, J. (2015). Developing pervasive multi-agent systems with nature-inspired coordination. Pervasive and Mobile Computing, 17, 236-252. doi:10.1016/j.pmcj.2014.12.002 es_ES
dc.description.references Severins, M., Klinkenberg, D., & Heesterbeek, H. (2007). Effects of heterogeneity in infection-exposure history and immunity on the dynamics of a protozoan parasite. Journal of The Royal Society Interface, 4(16), 841-849. doi:10.1098/rsif.2007.1061 es_ES
dc.description.references Šperka, R., & Spišák, M. (2013). TRANSACTION COSTS INFLUENCE ON THE STABILITY OF FINANCIAL MARKET: AGENT-BASED SIMULATION. Journal of Business Economics and Management, 14(Supplement_1), S1-S12. doi:10.3846/16111699.2012.701227 es_ES
dc.description.references Jong, J. de, Stellingwerff, L., & Pazienza, G. E. (2013). Eve: A Novel Open-Source Web-Based Agent Platform. 2013 IEEE International Conference on Systems, Man, and Cybernetics. doi:10.1109/smc.2013.265 es_ES
dc.description.references Kilkki, O., Kangasrääsiö, A., Nikkilä, R., Alahäivälä, A., & Seilonen, I. (2014). Agent-based modeling and simulation of a smart grid: A case study of communication effects on frequency control. Engineering Applications of Artificial Intelligence, 33, 91-98. doi:10.1016/j.engappai.2014.04.007 es_ES
dc.description.references Malik, F. H., & Lehtonen, M. (2016). A review: Agents in smart grids. Electric Power Systems Research, 131, 71-79. doi:10.1016/j.epsr.2015.10.004 es_ES
dc.description.references Nikolic, I., & Dijkema, G. P. J. (2010). On the development of agent-based models for infrastructure evolution. International Journal of Critical Infrastructures, 6(2), 148. doi:10.1504/ijcis.2010.031072 es_ES
dc.description.references Iturriza, M., Labaka, L., Sarriegi, J. M., & Hernantes, J. (2018). Modelling methodologies for analysing critical infrastructures. Journal of Simulation, 12(2), 128-143. doi:10.1080/17477778.2017.1418640 es_ES
dc.description.references Ryu, D. H., Kim, H., & Um, K. (2009). Reducing security vulnerabilities for critical infrastructure. Journal of Loss Prevention in the Process Industries, 22(6), 1020-1024. doi:10.1016/j.jlp.2009.07.015 es_ES
dc.description.references Bernieri, G., Etchevés Miciolino, E., Pascucci, F., & Setola, R. (2017). Monitoring system reaction in cyber-physical testbed under cyber-attacks. Computers & Electrical Engineering, 59, 86-98. doi:10.1016/j.compeleceng.2017.02.010 es_ES
dc.description.references Taormina, R., Galelli, S., Tippenhauer, N. O., Salomons, E., Ostfeld, A., Eliades, D. G., … Ohar, Z. (2018). Battle of the Attack Detection Algorithms: Disclosing Cyber Attacks on Water Distribution Networks. Journal of Water Resources Planning and Management, 144(8), 04018048. doi:10.1061/(asce)wr.1943-5452.0000969 es_ES
dc.description.references Bretas, A. S., Bretas, N. G., Carvalho, B., Baeyens, E., & Khargonekar, P. P. (2017). Smart grids cyber-physical security as a malicious data attack: An innovation approach. Electric Power Systems Research, 149, 210-219. doi:10.1016/j.epsr.2017.04.018 es_ES
dc.description.references Cui, L., Hu, J., Park, B. B., & Bujanovic, P. (2018). Development of a simulation platform for safety impact analysis considering vehicle dynamics, sensor errors, and communication latencies: Assessing cooperative adaptive cruise control under cyber attack. Transportation Research Part C: Emerging Technologies, 97, 1-22. doi:10.1016/j.trc.2018.10.005 es_ES
dc.description.references Liang, G., Weller, S. R., Zhao, J., Luo, F., & Dong, Z. Y. (2019). A Framework for Cyber-Topology Attacks: Line-Switching and New Attack Scenarios. IEEE Transactions on Smart Grid, 10(2), 1704-1712. doi:10.1109/tsg.2017.2776325 es_ES
dc.description.references He, D., Chan, S., & Guizani, M. (2015). Mobile application security: malware threats and defenses. IEEE Wireless Communications, 22(1), 138-144. doi:10.1109/mwc.2015.7054729 es_ES
dc.description.references Silk, H., Homer, M., & Gross, T. (2016). Design of Self-Organizing Networks: Creating Specified Degree Distributions. IEEE Transactions on Network Science and Engineering, 3(3), 147-158. doi:10.1109/tnse.2016.2586762 es_ES
dc.description.references Chen, Y., Guo, Z., Yang, X., Hu, Y., & Zhu, Q. (2017). Optimization of Coverage in 5G Self-Organizing Small Cell Networks. Mobile Networks and Applications, 23(6), 1502-1512. doi:10.1007/s11036-017-0983-x es_ES
dc.description.references Kuo, T.-W., Liou, B.-H., Lin, K. C.-J., & Tsai, M.-J. (2018). Deploying Chains of Virtual Network Functions: On the Relation Between Link and Server Usage. IEEE/ACM Transactions on Networking, 26(4), 1562-1576. doi:10.1109/tnet.2018.2842798 es_ES
dc.description.references Liang, C., Wen, F., & Wang, Z. (2019). Trust-based distributed Kalman filtering for target tracking under malicious cyber attacks. Information Fusion, 46, 44-50. doi:10.1016/j.inffus.2018.04.002 es_ES
dc.description.references Zañudo, J. G. T., Yang, G., & Albert, R. (2017). Structure-based control of complex networks with nonlinear dynamics. Proceedings of the National Academy of Sciences, 114(28), 7234-7239. doi:10.1073/pnas.1617387114 es_ES
dc.description.references Ding, J., Wen, C., Li, G., & Chen, Z. (2021). Key Nodes Selection in Controlling Complex Networks via Convex Optimization. IEEE Transactions on Cybernetics, 51(1), 52-63. doi:10.1109/tcyb.2018.2888953 es_ES
dc.description.references Fushimi, T., Saito, K., Ikeda, T., & Kazama, K. (2019). Estimating node connectedness in spatial network under stochastic link disconnection based on efficient sampling. Applied Network Science, 4(1). doi:10.1007/s41109-019-0187-3 es_ES
dc.description.references Zhang, X., Mahadevan, S., Sankararaman, S., & Goebel, K. (2018). Resilience-based network design under uncertainty. Reliability Engineering & System Safety, 169, 364-379. doi:10.1016/j.ress.2017.09.009 es_ES
dc.description.references Fu, C., Wang, Y., Gao, Y., & Wang, X. (2017). Complex networks repair strategies: Dynamic models. Physica A: Statistical Mechanics and its Applications, 482, 401-406. doi:10.1016/j.physa.2017.04.118 es_ES
dc.description.references Gu, J., Zhu, Y., Guo, L., Jiang, J., Chi, L., Li, W., … Cai, X. (2015). Recent Progress in Some Active Topics on Complex Networks. Journal of Physics: Conference Series, 604, 012007. doi:10.1088/1742-6596/604/1/012007 es_ES
dc.description.references Li, G., Deng, L., Xiao, G., Tang, P., Wen, C., Hu, W., … Stanley, H. E. (2018). Enabling Controlling Complex Networks with Local Topological Information. Scientific Reports, 8(1). doi:10.1038/s41598-018-22655-5 es_ES
dc.description.references Dilts, D. M., Boyd, N. P., & Whorms, H. H. (1991). The evolution of control architectures for automated manufacturing systems. Journal of Manufacturing Systems, 10(1), 79-93. doi:10.1016/0278-6125(91)90049-8 es_ES
dc.description.references Van Brussel, H., Wyns, J., Valckenaers, P., Bongaerts, L., & Peeters, P. (1998). Reference architecture for holonic manufacturing systems: PROSA. Computers in Industry, 37(3), 255-274. doi:10.1016/s0166-3615(98)00102-x es_ES
dc.description.references Bongaerts, L., Monostori, L., McFarlane, D., & Kádár, B. (2000). Hierarchy in distributed shop floor control. Computers in Industry, 43(2), 123-137. doi:10.1016/s0166-3615(00)00062-2 es_ES
dc.description.references Kai Cai, & Wonham, W. M. (2010). Supervisor Localization: A Top-Down Approach to Distributed Control of Discrete-Event Systems. IEEE Transactions on Automatic Control, 55(3), 605-618. doi:10.1109/tac.2009.2039237 es_ES
dc.description.references Duffie, N. A., & Piper, R. S. (1987). Non-hierarchical control of a flexible manufacturing cell. Robotics and Computer-Integrated Manufacturing, 3(2), 175-179. doi:10.1016/0736-5845(87)90099-8 es_ES
dc.description.references McFarlane, D. C., & Bussmann, S. (2003). Holonic Manufacturing Control: Rationales, Developments and Open Issues. Agent-Based Manufacturing, 303-326. doi:10.1007/978-3-662-05624-0_13 es_ES
dc.description.references Kollingbaum, M., Heikkilä, T., Peeters, P., Matson, J., Valckenaers, P., McFarlane, D., & Bluemink, G.-J. (2000). Emergent flow shop control based on MASCADA agents. IFAC Proceedings Volumes, 33(20), 187-192. doi:10.1016/s1474-6670(17)38047-3 es_ES
dc.description.references McFarlane, D., Sarma, S., Chirn, J. L., Wong, C. ., & Ashton, K. (2003). Auto ID systems and intelligent manufacturing control. Engineering Applications of Artificial Intelligence, 16(4), 365-376. doi:10.1016/s0952-1976(03)00077-0 es_ES
dc.description.references Leitão, P. (2009). Agent-based distributed manufacturing control: A state-of-the-art survey. Engineering Applications of Artificial Intelligence, 22(7), 979-991. doi:10.1016/j.engappai.2008.09.005 es_ES
dc.description.references Brintrup, A., McFarlane, D., Ranasinghe, D., Sanchez Lopez, T., & Owens, K. (2011). Will Intelligent Assets Take Off? Toward Self-Serving Aircraft. IEEE Intelligent Systems, 26(3), 66-75. doi:10.1109/mis.2009.89 es_ES
dc.description.references Brintrup, A., & Ledwoch, A. (2018). Supply network science: Emergence of a new perspective on a classical field. Chaos: An Interdisciplinary Journal of Nonlinear Science, 28(3), 033120. doi:10.1063/1.5010766 es_ES
dc.description.references Ledwoch, A., Brintrup, A., Mehnen, J., & Tiwari, A. (2018). Systemic Risk Assessment in Complex Supply Networks. IEEE Systems Journal, 12(2), 1826-1837. doi:10.1109/jsyst.2016.2596999 es_ES
dc.description.references Hearnshaw, E. J. S., & Wilson, M. M. J. (2013). A complex network approach to supply chain network theory. International Journal of Operations & Production Management, 33(4), 442-469. doi:10.1108/01443571311307343 es_ES
dc.description.references Marik, V., & McFarlane, D. (2005). Industrial Adoption of Agent-Based Technologies. IEEE Intelligent Systems, 20(1), 27-35. doi:10.1109/mis.2005.11 es_ES
dc.description.references Leitao, P., Karnouskos, S., Ribeiro, L., Lee, J., Strasser, T., & Colombo, A. W. (2016). Smart Agents in Industrial Cyber–Physical Systems. Proceedings of the IEEE, 104(5), 1086-1101. doi:10.1109/jproc.2016.2521931 es_ES
dc.description.references McFarlane, D., Giannikas, V., Wong, A. C. Y., & Harrison, M. (2013). Product intelligence in industrial control: Theory and practice. Annual Reviews in Control, 37(1), 69-88. doi:10.1016/j.arcontrol.2013.03.003 es_ES
dc.description.references Pagani, G. A., & Aiello, M. (2013). The Power Grid as a complex network: A survey. Physica A: Statistical Mechanics and its Applications, 392(11), 2688-2700. doi:10.1016/j.physa.2013.01.023 es_ES
dc.description.references Albert, R., Albert, I., & Nakarado, G. L. (2004). Structural vulnerability of the North American power grid. Physical Review E, 69(2). doi:10.1103/physreve.69.025103 es_ES
dc.description.references Pagani, G. A., & Aiello, M. (2014). Power grid complex network evolutions for the smart grid. Physica A: Statistical Mechanics and its Applications, 396, 248-266. doi:10.1016/j.physa.2013.11.022 es_ES
dc.description.references Moussawi, A., Derzsy, N., Lin, X., Szymanski, B. K., & Korniss, G. (2017). Limits of Predictability of Cascading Overload Failures in Spatially-Embedded Networks with Distributed Flows. Scientific Reports, 7(1). doi:10.1038/s41598-017-11765-1 es_ES
dc.description.references Roche, R., Blunier, B., Miraoui, A., Hilaire, V., & Koukam, A. (2010). Multi-agent systems for grid energy management: A short review. IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society. doi:10.1109/iecon.2010.5675295 es_ES
dc.description.references Dimeas, A. L., & Hatziargyriou, N. D. (2005). Operation of a Multiagent System for Microgrid Control. IEEE Transactions on Power Systems, 20(3), 1447-1455. doi:10.1109/tpwrs.2005.852060 es_ES
dc.description.references Lin, J., & Ban, Y. (2013). Complex Network Topology of Transportation Systems. Transport Reviews, 33(6), 658-685. doi:10.1080/01441647.2013.848955 es_ES
dc.description.references Lordan, O., Sallan, J. M., & Simo, P. (2014). Study of the topology and robustness of airline route networks from the complex network approach: a survey and research agenda. Journal of Transport Geography, 37, 112-120. doi:10.1016/j.jtrangeo.2014.04.015 es_ES
dc.description.references Crucitti, P., Latora, V., & Porta, S. (2006). Centrality measures in spatial networks of urban streets. Physical Review E, 73(3). doi:10.1103/physreve.73.036125 es_ES
dc.description.references Scellato, S., Cardillo, A., Latora, V., & Porta, S. (2006). The backbone of a city. The European Physical Journal B, 50(1-2), 221-225. doi:10.1140/epjb/e2006-00066-4 es_ES
dc.description.references Boeing, G. (2017). OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks. Computers, Environment and Urban Systems, 65, 126-139. doi:10.1016/j.compenvurbsys.2017.05.004 es_ES
dc.description.references Zheng, J.-F., Gao, Z.-Y., & Zhao, X.-M. (2007). Clustering and congestion effects on cascading failures of scale-free networks. Europhysics Letters (EPL), 79(5), 58002. doi:10.1209/0295-5075/79/58002 es_ES
dc.description.references Tian, W., Dai, X., & Hu, M. (2018). Systemic Congestion Propagation in the Airspace Network. Mathematical Problems in Engineering, 2018, 1-12. doi:10.1155/2018/7171486 es_ES
dc.description.references Baronti, F., Vazquez, S., & Chow, M.-Y. (2018). Modeling, Control, and Integration of Energy Storage Systems in E-Transportation and Smart Grid. IEEE Transactions on Industrial Electronics, 65(8), 6548-6551. doi:10.1109/tie.2018.2810658 es_ES
dc.description.references Lygeros, J., Godbole, D. N., & Broucke, M. (2000). A fault tolerant control architecture for automated highway systems. IEEE Transactions on Control Systems Technology, 8(2), 205-219. doi:10.1109/87.826792 es_ES
dc.description.references Herrera, M., Abraham, E., & Stoianov, I. (2016). A Graph-Theoretic Framework for Assessing the Resilience of Sectorised Water Distribution Networks. Water Resources Management, 30(5), 1685-1699. doi:10.1007/s11269-016-1245-6 es_ES
dc.description.references Di Nardo, A., Giudicianni, C., Greco, R., Herrera, M., & Santonastaso, G. (2018). Applications of Graph Spectral Techniques to Water Distribution Network Management. Water, 10(1), 45. doi:10.3390/w10010045 es_ES
dc.description.references Candelieri, A., & Archetti, F. (2014). Smart water in urban distribution networks: limited financial capacity and Big Data analytics. Urban Water II. doi:10.2495/uw140061 es_ES
dc.description.references Herrera, M., Izquierdo, J., Pérez-García, R., & Montalvo, I. (2012). Multi-agent adaptive boosting on semi-supervised water supply clusters. Advances in Engineering Software, 50, 131-136. doi:10.1016/j.advengsoft.2012.02.005 es_ES
dc.description.references Ayala-Cabrera, D., Herrera, M., Izquierdo, J., & Pérez-García, R. (2014). GPR data analysis using multi-agent and clustering approaches: A tool for technical management of water supply systems. Digital Signal Processing, 27, 140-149. doi:10.1016/j.dsp.2013.12.012 es_ES
dc.description.references Figueiredo, J., Botto, M. A., & Rijo, M. (2013). SCADA system with predictive controller applied to irrigation canals. Control Engineering Practice, 21(6), 870-886. doi:10.1016/j.conengprac.2013.01.008 es_ES
dc.description.references García, C. E., Prett, D. M., & Morari, M. (1989). Model predictive control: Theory and practice—A survey. Automatica, 25(3), 335-348. doi:10.1016/0005-1098(89)90002-2 es_ES
dc.description.references Bliek, F. W., van den Noort, A., Roossien, B., Kamphuis, R., de Wit, J., van der Velde, J., & Eijgelaar, M. (2011). The role of natural gas in smart grids. Journal of Natural Gas Science and Engineering, 3(5), 608-616. doi:10.1016/j.jngse.2011.07.008 es_ES
dc.description.references Brown, H. E., Suryanarayanan, S., & Heydt, G. T. (2010). Some Characteristics of Emerging Distribution Systems Considering the Smart Grid Initiative. The Electricity Journal, 23(5), 64-75. doi:10.1016/j.tej.2010.05.005 es_ES
dc.description.references Chacón, E., Besembel, I., & Hennet, J. C. (2004). Coordination and optimization in oil and gas production complexes. Computers in Industry, 53(1), 17-37. doi:10.1016/j.compind.2003.06.001 es_ES
dc.description.references Ameli, H., Qadrdan, M., & Strbac, G. (2017). Value of gas network infrastructure flexibility in supporting cost effective operation of power systems. Applied Energy, 202, 571-580. doi:10.1016/j.apenergy.2017.05.132 es_ES
dc.description.references Newman, M. E. J. (2004). Analysis of weighted networks. Physical Review E, 70(5). doi:10.1103/physreve.70.056131 es_ES
dc.description.references Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97-125. doi:10.1016/j.physrep.2012.03.001 es_ES
dc.description.references Schaub, M. T., Delvenne, J.-C., Lambiotte, R., & Barahona, M. (2019). Multiscale dynamical embeddings of complex networks. Physical Review E, 99(6). doi:10.1103/physreve.99.062308 es_ES
dc.description.references Raab, A., Lauth, E., Strunz, K., & Göhlich, D. (2019). Implementation Schemes for Electric Bus Fleets at Depots with Optimized Energy Procurements in Virtual Power Plant Operations. World Electric Vehicle Journal, 10(1), 5. doi:10.3390/wevj10010005 es_ES
dc.description.references Giudicianni, C., Herrera, M., di Nardo, A., Carravetta, A., Ramos, H. M., & Adeyeye, K. (2020). Zero-net energy management for the monitoring and control of dynamically-partitioned smart water systems. Journal of Cleaner Production, 252, 119745. doi:10.1016/j.jclepro.2019.119745 es_ES
dc.description.references Zhang, S., Tong, H., Xu, J., & Maciejewski, R. (2019). Graph convolutional networks: a comprehensive review. Computational Social Networks, 6(1). doi:10.1186/s40649-019-0069-y es_ES
dc.description.references Manessi, F., Rozza, A., & Manzo, M. (2020). Dynamic graph convolutional networks. Pattern Recognition, 97, 107000. doi:10.1016/j.patcog.2019.107000 es_ES
dc.description.references Chen, S.-H., & Venkatachalam, R. (2017). Agent-based modelling as a foundation for big data. Journal of Economic Methodology, 24(4), 362-383. doi:10.1080/1350178x.2017.1388964 es_ES
dc.description.references Da Silva, F. L., Warnell, G., Costa, A. H. R., & Stone, P. (2019). Agents teaching agents: a survey on inter-agent transfer learning. Autonomous Agents and Multi-Agent Systems, 34(1). doi:10.1007/s10458-019-09430-0 es_ES
dc.description.references Leitão, P., Colombo, A. W., & Karnouskos, S. (2016). Industrial automation based on cyber-physical systems technologies: Prototype implementations and challenges. Computers in Industry, 81, 11-25. doi:10.1016/j.compind.2015.08.004 es_ES
dc.description.references Yao, X., Zhou, J., Lin, Y., Li, Y., Yu, H., & Liu, Y. (2017). Smart manufacturing based on cyber-physical systems and beyond. Journal of Intelligent Manufacturing, 30(8), 2805-2817. doi:10.1007/s10845-017-1384-5 es_ES
dc.description.references Airlangga, G., & Liu, A. (2019). Initial Machine Learning Framework Development of Agriculture Cyber Physical Systems. Journal of Physics: Conference Series, 1196, 012065. doi:10.1088/1742-6596/1196/1/012065 es_ES
dc.description.references Salah, K., Rehman, M. H. U., Nizamuddin, N., & Al-Fuqaha, A. (2019). Blockchain for AI: Review and Open Research Challenges. IEEE Access, 7, 10127-10149. doi:10.1109/access.2018.2890507 es_ES


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