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An Agent-Based Virtual Organization for Risk Control in Large Enterprises

  • M. Lourdes BorrajoEmail author
  • Juan M. Corchado
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 877)

Abstract

At present, business decision making is a crucial task in every enterprise as it allows to minimize risks and maximize benefits. For effective decision making, large corporations and enterprises need tools that will help them detect inefficient activities in their internal processes. This article presents a virtual organization of agents designed to detect risky situations and provide recommendations to the internal auditors of large corporations. Each agent within the virtual organization facilitates the interconnection of enterprises with the central decision node of the corporation. The core of the agent-based virtual organization consists of two agents: one that is specialized in detecting risky situations in all aspects of business enterprise and an advisor agent which communicates with the evaluator agents of the different departments of a business and provides decision support services. This paper presents a real-case scenario which includes small and medium enterprises, the results demonstrate the feasibility of the proposed architecture.

Keywords

Agent-based virtual organizations Internal audit Case 

Notes

Acknowledgements

This work has been funded by the Spanish Ministry of Science and Innovation (TIN2015-65515-C4-3-R).

References

  1. 1.
    Yañez, J.C., Borrajo, L., Corchado, J.M.: A case-based reasoning system for business internal control. In: Fourth International ICSC Symposium. Soft Computing and Intelligent Systems for Industry, Paisley, Scotland, United Kingdom, 26–29 June 2001Google Scholar
  2. 2.
    Mas, J., Ramió, C.: La Auditoría Operativa en la Práctica. Ed. Marcombo, Barcelona (1997)Google Scholar
  3. 3.
    Rodriguez, S., Julián, V., Bajo, J., Carrascosa, C., Botti, V., Corchado, J.M.: Agent-based virtual organization architecture. Eng. Appl. Artif. Intell. 24(5), 895–910 (2011)CrossRefGoogle Scholar
  4. 4.
    Bajo, J., De Paz, Y., De Paz, J.F., Corchado, J.M.: Integrating case planning and RPTW neuronal networks to construct an intelligent environment for health care. Expert Syst. Appl. 36(3), 5844–5858 (2009)CrossRefGoogle Scholar
  5. 5.
    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
  6. 6.
    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
  7. 7.
    Costa, Â., Novais, P., Corchado, J.M., Neves, J.: Increased performance and better patient attendance in an hospital with the use of smart agendas. Log. J. IGPL 20(4), 689–698 (2012).  https://doi.org/10.1093/jigpal/jzr021MathSciNetCrossRefGoogle Scholar
  8. 8.
    García, E., Rodríguez, S., Martín, B., Zato, C., Pérez, B.: MISIA: middleware infrastructure to simulate intelligent agents. Adv. Intell. Soft Comput. 9, 107–116 (2011).  https://doi.org/10.1007/978-3-642-19934-9_14CrossRefGoogle Scholar
  9. 9.
    Rodríguez, S., De La Prieta, F., Tapia, D.I., Corchado, J.M.: Agents and computer vision for processing stereoscopic images. In: Corchado, E., Graña Romay, M., Manhaes Savio, A. (eds.) HAIS 2010. LNCS (LNAI, LNB), vol. 6077, pp. 93–100. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-13803-4_12
  10. 10.
    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
  11. 11.
    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
  12. 12.
    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
  13. 13.
    Valanarasu, R., Christy, A.: Risk assessment and management in enterprise resource planning by advanced system engineering theory. Int. J. Bus. Intell. Data Min. 13(1–3), 3–14 (2018)CrossRefGoogle Scholar
  14. 14.
    Namatame, A.: Agent-based modeling of economic instability. Stud. Comput. Intell. 753, 255–265 (2018)Google Scholar
  15. 15.
    Ai, J., Brockett, P.L., Wang, T.: Optimal enterprise risk management and decision making with shared and dependent risks. J. Risk Insur. 84(4), 1127–1169 (2017)Google Scholar
  16. 16.
    Callahan, C., Soileau, J.: Does enterprise risk management enhance operating performance? Adv. Account. 37, 122–139 (2017)CrossRefGoogle Scholar
  17. 17.
    Raschke, R.L., Mann, A.: Enterprise content risk management: a conceptual framework for digital asset risk management. J. Emerg. Technol. Account. 14(1), 57–62 (2017)CrossRefGoogle Scholar
  18. 18.
    Tapia, D.I., Rodríguez, S., Bajo, J., Corchado, J.M.: FUSION@, a SOA-based multi-agent architecture. Adv. Soft Comput. 50, 99–107 (2009)CrossRefGoogle Scholar
  19. 19.
    Bremer, J., Lehnhoff, S.: Decentralized coalition formation with agent-based combinatorial heuristics. ADCAIJ: Adv. Distrib. Comput. Artif. Intell. 6(3), 29–44 (2017)CrossRefGoogle Scholar
  20. 20.
    Rodríguez, S., Pérez-Lancho, B., De Paz, J.F., Bajo, J., Corchado, J.M.: Ovamah: multi-agent-based adaptive virtual organizations, In: 12th International Conference on Information Fusion, FUSION 2009, pp. 990–997 (2009). https://ieeexplore.ieee.org/document/5203822/
  21. 21.
    Zato, C., Villarrubia, G., Sánchez, A., Barri, I., Rubión, E., Fernandez, A., Rebate, C., Cabo, J.A., Álamo, R., Sanz, J., Seco, J., Bajo, J., Corchado, J.M.: PANGEA - platform for automatic construction of organizations of intelligent agents. In: Advances in Intelligent and Soft Computing (AISC), vol. 151, pp. 229–239 (2012)Google Scholar
  22. 22.
    Bajo, J., Borrajo, M.L., De Paz, J.F., Corchado, J.M., Pellicer, M.A.: A multi-agent system for web-based risk management in small and medium business. Expert Syst. Appl. 39(8), 6921–6931 (2012)CrossRefGoogle Scholar
  23. 23.
    Corchado, J.M., Pavón, J., Corchado, E.S., Castillo, L.F.: Development of CBR-BDI agents: a tourist guide application. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI, LNB), vol. 3155, pp. 547–559. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-28631-8
  24. 24.
    Laza, R., Pavon, R., Corchado, J.M.: A reasoning model for CBR_BDI agents using an adaptable fuzzy inference system. In: Conejo, R., Urretavizcaya, M., Pérez-de-la-Cruz, J.L. (eds.) TTIA 2003. LNCS (LNAI, LNB), vol. 3040, pp. 96–106. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-25945-9_10
  25. 25.
    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
  26. 26.
    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, 187 (2009).  https://doi.org/10.1186/1471-2105-10-187CrossRefGoogle Scholar
  27. 27.
    Corchado, J.A., Aiken, J., Corchado, E.S., Lefevre, N., Smyth, T.: Quantifying the ocean’s CO2 budget with a CoHeL-IBR system. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS, vol. 3155, pp. 533–546. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-28631-8_39
  28. 28.
    Corchado, J.M., Borrajo, M.L., Pellicer, M.A., Yáñez, J.C.: Neuro-symbolic system for business internal control. In: Perner, P. (ed.) ICDM 2004. LNCS, vol. 3275, pp. 1–10. Springer, Heidelberg (2004).  https://doi.org/10.1007/978-3-540-30185-1_1
  29. 29.
    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
  30. 30.
    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
  31. 31.
    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
  32. 32.
    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
  33. 33.
    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), 173–185 (2002)Google Scholar
  34. 34.
    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: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS (LNAI, LNB), vol. 2689, pp. 107–121. Springer, Heidelberg (2003).  https://doi.org/10.1007/3-540-45006-8_11
  35. 35.
    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
  36. 36.
    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
  37. 37.
    Corchado, E., MacDonald, D., Fyfe, C.: Optimal projections of high dimensional data. In: The 2002 IEEE International Conference on Data Mining, ICDM 2002, Maebashi TERRSA, Maebashi City, Japan, 9–12 December 2002. IEEE Computer Society (2002)Google Scholar
  38. 38.
    Fyfe, C., Corchado, E.: Maximum likelihood Hebbian rules. In: 10th European Symposium on Artificial Neural Networks, ESANN 2002, Bruges, 24–25–26 April 2002Google Scholar
  39. 39.
    Fyfe, C., Corchado E.: A new neural implementation of exploratory projection pursuit. In: Yin, H., Allinson, N., Freeman, R., Keane, J., Hubbard, S. (eds.) IDEAL 2002. LNCS, vol. 2412, pp. 512–517. Springer, Heidelberg (2002).  https://doi.org/10.1007/3-540-45675-9_77
  40. 40.
    Fyfe, C., MacDonald, D.: ε-insensitive Hebbian learning. Neuro Comput. 47(1–4), 33–57 (2001)Google Scholar
  41. 41.
    Oja, E.: Neural networks, principal components and subspaces. Int. J. Neural Syst. 1, 61–68 (1989)CrossRefGoogle Scholar
  42. 42.
    Oja, E., Ogawa, H., Wangviwattana, J.: Principal components analysis by homogeneous neural networks, part 1, the weighted subspace criterion. IEICE Trans. Inf. Syst. E75D, 366–375 (1992)Google Scholar
  43. 43.
    Dietterich, T.G.: Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput. 10(7), 1895–1923 (1998)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.University of VigoVigoSpain
  2. 2.University of SalamancaSalamancaSpain
  3. 3.Osaka Institute of TechnologyOsakaJapan

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