- -

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

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

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

Mostrar el registro completo del ítem

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

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/162561

Ficheros en el ítem

Metadatos del ítem

Título: Multi-Agent Systems and Complex Networks: Review and Applications in Systems Engineering
Autor: Herrera, Manuel Pérez-Hernández, Marco Parlikad, Ajith Kumar Izquierdo Sebastián, Joaquín
Entidad UPV: Universitat Politècnica de València. Instituto Universitario de Matemática Multidisciplinar - Institut Universitari de Matemàtica Multidisciplinària
Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada
Fecha difusión:
Resumen:
[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 ...[+]
Palabras clave: Systems engineering , Complex networks , Multi-agent systems , Optimisation , Processes systems engineering , Agent-based control
Derechos de uso: Reconocimiento (by)
Fuente:
Processes. (eissn: 2227-9717 )
DOI: 10.3390/pr8030312
Editorial:
MDPI AG
Versión del editor: https://doi.org/10.3390/pr8030312
Código del Proyecto:
info:eu-repo/grantAgreement/UKRI//EP%2FR004935%2F1/GB/Next Generation Converged Digital infrastructure (NG-CDI)/
Agradecimientos:
This research was funded by the EPSRC and BT Prosperity Partnership project: Next Generation Converged Digital Infrastructure, grant number EP/R004935/1.
Tipo: Artículo

References

Strogatz, S. H. (2001). Exploring complex networks. Nature, 410(6825), 268-276. doi:10.1038/35065725

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

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 [+]
Strogatz, S. H. (2001). Exploring complex networks. Nature, 410(6825), 268-276. doi:10.1038/35065725

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

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

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

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

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

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

Charyyev, B., & Gunes, M. H. (2019). Complex network of United States migration. Computational Social Networks, 6(1). doi:10.1186/s40649-019-0061-6

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

Manuel, P. (2010). Computational Aspects of Carbon and Boron Nanotubes. Molecules, 15(12), 8709-8722. doi:10.3390/molecules15128709

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

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

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

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

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

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

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

Guimerà, R., & Nunes Amaral, L. A. (2005). Functional cartography of complex metabolic networks. Nature, 433(7028), 895-900. doi:10.1038/nature03288

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

Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440-442. doi:10.1038/30918

Barabási, A.-L. (2009). Scale-Free Networks: A Decade and Beyond. Science, 325(5939), 412-413. doi:10.1126/science.1173299

Viana, M. P., Strano, E., Bordin, P., & Barthelemy, M. (2013). The simplicity of planar networks. Scientific Reports, 3(1). doi:10.1038/srep03495

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

Diet, A., & Barthelemy, M. (2018). Towards a classification of planar maps. Physical Review E, 98(6). doi:10.1103/physreve.98.062304

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

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

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

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

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

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

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

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

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

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

Freeman, L. C. (1977). A Set of Measures of Centrality Based on Betweenness. Sociometry, 40(1), 35. doi:10.2307/3033543

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

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

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

Katz, L. (1953). A new status index derived from sociometric analysis. Psychometrika, 18(1), 39-43. doi:10.1007/bf02289026

Serrano, M. Á., & Boguñá, M. (2006). Clustering in complex networks. I. General formalism. Physical Review E, 74(5). doi:10.1103/physreve.74.056114

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

Noldus, R., & Van Mieghem, P. (2015). Assortativity in complex networks. Journal of Complex Networks, 3(4), 507-542. doi:10.1093/comnet/cnv005

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

Gao, J., Barzel, B., & Barabási, A.-L. (2016). Universal resilience patterns in complex networks. Nature, 530(7590), 307-312. doi:10.1038/nature16948

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

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

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

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

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

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

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

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

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

Hui, K.-P. (2007). Monte Carlo Network Reliability Ranking Estimation. IEEE Transactions on Reliability, 56(1), 50-57. doi:10.1109/tr.2006.890898

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

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

Morone, F., & Makse, H. A. (2015). Influence maximization in complex networks through optimal percolation. Nature, 524(7563), 65-68. doi:10.1038/nature14604

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

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

Jalili, M., & Perc, M. (2017). Information cascades in complex networks. Journal of Complex Networks. doi:10.1093/comnet/cnx019

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

Lawyer, G. (2015). Understanding the influence of all nodes in a network. Scientific Reports, 5(1). doi:10.1038/srep08665

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

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

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

Bardet, J.-P., & Little, R. (2014). Epidemiology of urban water distribution systems. Water Resources Research, 50(8), 6447-6465. doi:10.1002/2013wr015017

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

Kim, H., & Anderson, R. (2012). Temporal node centrality in complex networks. Physical Review E, 85(2). doi:10.1103/physreve.85.026107

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

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

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

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

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

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

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

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

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

Osat, S., Faqeeh, A., & Radicchi, F. (2017). Optimal percolation on multiplex networks. Nature Communications, 8(1). doi:10.1038/s41467-017-01442-2

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

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

Nwana, H. S. (1996). Software agents: an overview. The Knowledge Engineering Review, 11(3), 205-244. doi:10.1017/s026988890000789x

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

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

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

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

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

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

Wooldridge, M., & Jennings, N. R. (1995). Intelligent agents: theory and practice. The Knowledge Engineering Review, 10(2), 115-152. doi:10.1017/s0269888900008122

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

HEXMOOR, H. (2002). A model of absolute autonomy and power: Toward group effects. Connection Science, 14(4), 323-333. doi:10.1080/0954009021000068727

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

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

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

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

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

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

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

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

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

FIPA ACL Message Structure Specificationhttp://www.fipa.org/specs/fipa00061/SC00061G.html

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Marik, V., & McFarlane, D. (2005). Industrial Adoption of Agent-Based Technologies. IEEE Intelligent Systems, 20(1), 27-35. doi:10.1109/mis.2005.11

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

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

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

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

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

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

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

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

Lin, J., & Ban, Y. (2013). Complex Network Topology of Transportation Systems. Transport Reviews, 33(6), 658-685. doi:10.1080/01441647.2013.848955

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Newman, M. E. J. (2004). Analysis of weighted networks. Physical Review E, 70(5). doi:10.1103/physreve.70.056131

Holme, P., & Saramäki, J. (2012). Temporal networks. Physics Reports, 519(3), 97-125. doi:10.1016/j.physrep.2012.03.001

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

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

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

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

Manessi, F., Rozza, A., & Manzo, M. (2020). Dynamic graph convolutional networks. Pattern Recognition, 97, 107000. doi:10.1016/j.patcog.2019.107000

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

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

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

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

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

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

[-]

recommendations

 

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro completo del ítem