Abstract
Industry 4.0 has revolutionized the recent years because the requirements in all domains of manufacturing, production or sales are dynamics and uncertainty and with them the challenges such as emerging technologies, great volumes of data and to make decisions in real time. This paper describes the advantage of a self-organized multiagent system to addresses the problem of data and how process them in Industry 4.0 environment.
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Adam, E., Grislin-Le Strugeon, E., Mandiau, R.: MAS architecture and knowledge model for vehicles data communication. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 1(1) (2012)
Baruque, B., Corchado, E., Mata, A., Corchado, J.M.: A forecasting solution to the oil spill problem based on a hybrid intelligent system. Inf. Sci. 180(10), 2029–2043 (2010). https://doi.org/10.1016/j.ins.2009.12.032
Buciarelli, E., Silvestri, M., González, S.R.: Decision economics, in commemoration of the birth centennial of Herbert A. Simon 1916–2016 (Nobel Prize in Economics 1978). In: Distributed Computing and Artificial Intelligence, 13th International Conference. Advances in Intelligent Systems and Computing, vol. 475. Springer (2016)
Chamoso, P., Rivas, A., Martín-Limorti, J.J., Rodríguez, S.: A hash based image matching algorithm for social networks. In: Advances in Intelligent Systems and Computing, vol. 619, pp. 183–190 (2018)
Choon, Y.W., Mohamad, M.S., Deris, S., Illias, R.M., Chong, C.K., Chai, L.E., Corchado, J.M.: Differential bees flux balance analysis with OptKnock for in silico microbial strains optimization. PLoS ONE 9(7) (2014)
Corchado, J.A., Aiken, J., Corchado, E.S., Lefevre, N., Smyth, T.: Quantifying the Ocean’s CO2 budget with a CoHeL-IBR system. In: Proceedings of Advances in Case-Based Reasoning, vol. 3155, pp. 533–546 (2004)
Corchado, J.M., Aiken, J.: Hybrid artificial intelligence methods in oceanographic forecast models. IEEE Trans. Syst. Man Cybern. Part C-Appl. Rev. 32(4), 307–313 (2002). https://doi.org/10.1109/tsmcc.2002.806072
Corchado, J.M., Fyfe, C.: Unsupervised neural method for temperature forecasting. Artif. Intell. Eng. 13(4), 351–357 (1999). https://doi.org/10.1016/S0954-1810(99)00007-2
Corchado, J.M., Borrajo, M.L., Pellicer, M.A., Yáñez, J.C.: Neuro-symbolic system for business internal control. In: Industrial Conference on Data Mining, pp. 1–10 (2004)
Corchado, J.M., Corchado, E.S., Aiken, J., Fyfe, C., Fernandez, F., Gonzalez, M.: Maximum likelihood Hebbian learning based retrieval method for CBR systems. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 2689, pp. 107–121 (2003). https://doi.org/10.1007/3-540-45006-8_11
Corchado, J.M., Pavón, J., Corchado, E.S., Castillo, L.F.: Development of CBR-BDI agents: a tourist guide application. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3155, pp. 547–559 (2004)
Corchado, J., Fyfe, C., Lees, B.: Unsupervised learning for financial forecasting. In: Proceedings of the IEEE/IAFE/INFORMS 1998 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No. 98TH8367), pp. 259–263 (1998). https://doi.org/10.1109/CIFER.1998.690316
Costa, Â., Novais, P., Corchado, J.M., Neves, J.: Increased performance and better patient attendance in an hospital with the use of smart agendas. Logic J. IGPL 20(4), 689–698 (2012)
De La Prieta, F., Navarro, M., García, J.A., González, R., Rodríguez, S.: Multi-agent system for controlling a cloud computing environment. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNAI, vol. 8154 (2013)
Fdez-Riverola, F., Corchado, J.M.: CBR based system for forecasting red tides. Knowl.-Based Syst. 16(5–6 SPEC.), 321–328 (2003). https://doi.org/10.1016/S0950-7051(03)00034-0
Fdez-Rtverola, F., Corchado, J.M.: FSfRT: forecasting system for red tides. Appl. Intell. 21(3), 251–264 (2004). https://doi.org/10.1023/B:APIN.0000043558.52701.b1
Fernández-Riverola, F., Díaz, F., Corchado, J.M.: Reducing the memory size of a Fuzzy case-based reasoning system applying rough set techniques. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 37(1), 138–146 (2007)
Fyfe, C., Corchado, J.: A comparison of Kernel methods for instantiating case based reasoning systems. Adv. Eng. Inform. 16(3), 165–178 (2002). https://doi.org/10.1016/S1474-0346(02)00008-3
Fyfe, C., Corchado, J.M.: Automating the construction of CBR systems using kernel methods. Int. J. Intell. Syst. 16(4), 571–586 (2001). https://doi.org/10.1002/int.1024
García Coria, J.A., Castellanos-Garzón, J.A., Corchado, J.M.: Intelligent business processes composition based on multi-agent systems. Expert Syst. Appl. 41(4 PART 1), 1189–1205 (2014). https://doi.org/10.1016/j.eswa.2013.08.003
García, E., Rodríguez, S., Martín, B., Zato, C., Pérez, B.: MISIA: Middleware infrastructure to simulate intelligent agents. In: Advances in Intelligent and Soft Computing, vol. 91 (2011). https://doi.org/10.1007/978-3-642-19934-9_14
Glez-Bedia, M., Corchado, J.M., Corchado, E.S., Fyfe, C.: Analytical model for constructing deliberative agents. Int. J. Eng. Intell. Syst. Electr. Eng. Commun. 10(3) (2002)
Glez-Peña, D., Díaz, F., Hernández, J.M., Corchado, J.M., Fdez-Riverola, F.: geneCBR: a translational tool for multiple-microarray analysis and integrative information retrieval for aiding diagnosis in cancer research. BMC Bioinform. 10 (2009). https://doi.org/10.1186/1471-2105-10-187
González Briones, A., Chamoso, P., Barriuso A.: Review of the main security problems with multi-agent systems used in e-commerce applications. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 5 (2016)
Isaza, G., Mejía, M., Castillo, L.F., Morales, A., Duque, N.: Network management using multi-agents system. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 1(3) (2012)
Kou, G., González-Crespo, R., Corchado, J.M., Herrera-Viedma, E.: Solving multi-criteria group decision making problems under environments with a high number of alternatives using fuzzy ontologies and multi-granular linguistic modelling methods. Knowl.-Based Syst. 137, 54–64 (2017)
Laza, R., Pavn, R., Corchado, J.M.: A reasoning model for CBR_BDI agents using an adaptable fuzzy inference system. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3040, pp. 96–106. Springer, Heidelberg (2004)
Li, T., Sun, S., Bolić, M., Corchado, J.M.: Algorithm design for parallel implementation of the SMC-PHD filter. Sig. Process. 119, 115–127 (2016). https://doi.org/10.1016/j.sigpro.2015.07.013
Li, T., Sun, S., Corchado, J.M., Siyau, M.F.: A particle dyeing approach for track continuity for the SMC-PHD filter. In: FUSION 2014 - 17th International Conference on Information Fusion (2014)
Li, T., Sun, S., Corchado, J.M., Siyau, M.F.: Random finite set-based Bayesian filters using magnitude-adaptive target birth intensity. In: FUSION 2014 - 17th International Conference on Information Fusion (2014)
Li, T.-C., Su, J.-Y., Liu, W., Corchado, J.M.: Approximate Gaussian conjugacy: parametric recursive filtering under nonlinearity, multimodality, uncertainty, and constraint, and beyond. Front. Inf. Technol. Electr. Eng. 18(12), 1913–1939 (2017)
Lima, A.C.E.S., De Castro, L.N., Corchado, J.M.: A polarity analysis framework for Twitter messages. Appl. Math. Comput. 270, 756–767 (2015). https://doi.org/10.1016/j.amc.2015.08.059
Mata, A., Corchado, J.M.: Forecasting the probability of finding oil slicks using a CBR system. Expert Syst. Appl. 36(4), 8239–8246 (2009). https://doi.org/10.1016/j.eswa.2008.10.003
Méndez, J.R., Fdez-Riverola, F., Díaz, F., Iglesias, E.L., Corchado, J.M.: A comparative performance study of feature selection methods for the anti-spam filtering domain. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNAI, vol. 4065, pp. 106–120 (2006)
Méndez, J.R., Fdez-Riverola, F., Iglesias, E.L., Díaz, F., Corchado, J.M.: Tracking concept drift at feature selection stage in SpamHunting: an anti-spam instance-based reasoning system. In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNAI, vol. 4106, pp. 504–518. Morente-Molinera, J.A. (2006)
Omatu, S., Wada, T., Chamoso, P.: Odor classification using agent technology. DCAIJ Adv. Distrib. Comput. Artif. Intell. J. 2(4) (2013)
Palomino, C.G., Nunes, C.S., Silveira, R.A., González, S.R., Nakayama, M.K.: Adaptive agent-based environment model to enable the teacher to create an adaptive class. In: Advances in Intelligent Systems and Computing, vol. 617 (2017). https://doi.org/10.1007/978-3-319-60819-8_3
Peñaranda, C., Agüero, J., Carrascosa, C., Rebollo, M., Julián, V.: An agent-based approach for a smart transport system. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 5(2) (2016)
Pinto, T., Gazafroudi, A.S., Prieto-Castrillo, F., Santos, G., Silva, F., Corchado, J.M., Vale, Z.: Reserve costs allocation model for energy and reserve market simulation. In: 2017 19th International Conference on Intelligent System Application to Power Systems, ISAP 2017, art. no. 8071410 (2017)
Redondo-Gonzalez, E., De Castro, L.N., Moreno-Sierra, J., Maestro De Las Casas, M.L., Vera-Gonzalez, V., Ferrari, D.G., Corchado, J.M.: Bladder carcinoma data with clinical risk factors and molecular markers: a cluster analysis. BioMed Res. Int. (2015). https://doi.org/10.1155/2015/168682
Rodríguez, S., De La Prieta, F., Tapia, D.I., Corchado, J.M.: Agents and computer vision for processing stereoscopic images. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). LNAI, vol. 6077 (2010). https://doi.org/10.1007/978-3-642-13803-4_12
Rodríguez, S., Gil, O., De La Prieta, F., Zato, C., Corchado, J.M., Vega, P., Francisco, M.: People detection and stereoscopic analysis using MAS. In: Proceedings of INES 2010 - 14th International Conference on Intelligent Engineering Systems (2010). https://doi.org/10.1109/INES.2010.5483855
Rodríguez, S., Tapia, D.I., Sanz, E., Zato, C., De La Prieta, F., Gil, O.: Cloud computing integrated into service-oriented multi-agent architecture. In: IFIP Advances in Information and Communication Technology. AICT, vol. 322 (2010). https://doi.org/10.1007/978-3-642-14341-0_29
Román Gallego, J.A.,, Rodríguez González, S.: Improvement in the distribution of services in multi-agent systems with SCODA. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 4(3) (2015)
Román, J.A., Rodríguez, S., de da Prieta, F.: Improving the distribution of services in MAS. Commun. Comput. Inf. Sci. 616 (2016). https://doi.org/10.1007/978-3-319-39387-2_4
Santos, G., Pinto, T., Vale, Z., Praça, I., Morais, H.: Enabling communications in heterogeneous multi-agent systems: electricity markets ontology. ADCAIJ Adv. Distrib. Comput. Artif. Intell. J. 5(2) (2016)
Sittón, I., Rodríguez, S.: Pattern extraction for the design of predictive models in industry 4.0. In: International Conference on Practical Applications of Agents and Multi-Agent Systems, pp. 258–261 (2017)
Tapia, D.I., Corchado, J.M.: An ambient intelligence based multi-agent system for alzheimer health care. Int. J. Ambient Comput. Intell. 1(1), 15–26 (2009). https://doi.org/10.4018/jaci.2009010102
Tapia, D.I., Fraile, J.A., Rodríguez, S., Alonso, R.S., Corchado, J.M.: Integrating hardware agents into an enhanced multi-agent architecture for Ambient Intelligence systems. Inf. Sci. 222, 47–65 (2013). https://doi.org/10.1016/j.ins.2011.05.002
Wang, X., Li, T., Sun, S., Corchado, J.M.: A survey of recent advances in particle filters and remaining challenges for multitarget tracking. Sensors 17(12), art. no. 2707 (2017)
Acknowledgments
I. Sittón Candanedo has been supported by IFARHU – SENACYT scholarship program (Government of Panama).
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Candanedo, I.S. (2019). A Self-organized Multiagent System for Industry 4.0. In: Rodríguez, S., et al. Distributed Computing and Artificial Intelligence, Special Sessions, 15th International Conference. DCAI 2018. Advances in Intelligent Systems and Computing, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-319-99608-0_55
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