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A Self-organized Multiagent System for Industry 4.0

  • Inés Sittón Candanedo
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 801)

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.

Keywords

Artificial intelligence Multiagent system Self-organized Industry 4.0 

Notes

Acknowledgments

I. Sittón Candanedo has been supported by IFARHU – SENACYT scholarship program (Government of Panama).

References

  1. 1.
    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)Google Scholar
  2. 2.
    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.2009.12.032CrossRefGoogle Scholar
  3. 3.
    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)Google Scholar
  4. 4.
    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)Google Scholar
  5. 5.
    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)CrossRefGoogle Scholar
  6. 6.
    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)Google Scholar
  7. 7.
    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.2002.806072CrossRefGoogle Scholar
  8. 8.
    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)00007-2CrossRefGoogle Scholar
  9. 9.
    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)Google Scholar
  10. 10.
    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
  11. 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)Google Scholar
  12. 12.
    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
  13. 13.
    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)MathSciNetCrossRefGoogle Scholar
  14. 14.
    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)Google Scholar
  15. 15.
    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)00034-0CrossRefGoogle Scholar
  16. 16.
    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.b1CrossRefGoogle Scholar
  17. 17.
    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)CrossRefGoogle Scholar
  18. 18.
    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)00008-3CrossRefGoogle Scholar
  19. 19.
    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.1024CrossRefzbMATHGoogle Scholar
  20. 20.
    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.2013.08.003CrossRefGoogle Scholar
  21. 21.
    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_14Google Scholar
  22. 22.
    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)Google Scholar
  23. 23.
    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/10.1186/1471-2105-10-187CrossRefGoogle Scholar
  24. 24.
    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)CrossRefGoogle Scholar
  25. 25.
    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)Google Scholar
  26. 26.
    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)CrossRefGoogle Scholar
  27. 27.
    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)CrossRefGoogle Scholar
  28. 28.
    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.2015.07.013CrossRefGoogle Scholar
  29. 29.
    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)Google Scholar
  30. 30.
    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)Google Scholar
  31. 31.
    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)CrossRefGoogle Scholar
  32. 32.
    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.2015.08.059CrossRefGoogle Scholar
  33. 33.
    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.2008.10.003CrossRefGoogle Scholar
  34. 34.
    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)CrossRefGoogle Scholar
  35. 35.
    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)Google Scholar
  36. 36.
    Omatu, S., Wada, T., Chamoso, P.: Odor classification using agent technology. DCAIJ Adv. Distrib. Comput. Artif. Intell. J. 2(4) (2013)Google Scholar
  37. 37.
    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_3Google Scholar
  38. 38.
    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)CrossRefGoogle Scholar
  39. 39.
    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)Google Scholar
  40. 40.
    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/10.1155/2015/168682CrossRefGoogle Scholar
  41. 41.
    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_12Google Scholar
  42. 42.
    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
  43. 43.
    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_29CrossRefGoogle Scholar
  44. 44.
    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)Google Scholar
  45. 45.
    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_4Google Scholar
  46. 46.
    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)CrossRefGoogle Scholar
  47. 47.
    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)Google Scholar
  48. 48.
    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.2009010102CrossRefGoogle Scholar
  49. 49.
    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.2011.05.002CrossRefGoogle Scholar
  50. 50.
    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)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Grupo GITCEUniversidad Tecnológica de PanamáPanama CityPanama

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