Skip to main content
SpringerLink
Log in

iEnsemble: A Framework for Committee Machine Based on Multiagent Systems with Reinforcement Learning

  • Conference paper
  • First Online:
Advances in Soft Computing (MICAI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10062))

Included in the following conference series:

Abstract

The Machine Learning is one of the areas of Artificial Intelligence whose objective is the development of computational techniques for knowledge and building systems able to acquire knowledge automatically. One of the main challenges of learning algorithms is to maximize generalization. Thus the board machine, or a combination of more of a learning machine approach known in literature with the denomination ensemble along with the theory agents, become a promising alternative in this challenge. In this context, this research proposes the iEnsemble framework, which aims to provide a model of the ensemble through a multi-agent system architecture, where generalization, combination and learning are made through agents, through the performance of their respective roles. In the proposal, the agents follow each their life cycle and also perform the iStacking algorithm. This algorithm is based on Stacking method, which uses the reinforcement learning to define the result of the Ensemble. To validate the initial proposal of the framework, some experiments have been performed and the results obtained and limitations are presented.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Barbosa. B.H.G.: Computação Evolucionária e Máquinas de Comitê na Identificação de Sistemas Não-Lineares. Tese. Belo Horizonte: Universidade Federal de Minas Gerais. Programa de Pós-Graduação em Engenharia Elétrica (2009). 224:il

    Google Scholar 

  2. Bellifemine, F., Caire, G., Greewood, D.: Developing Multiagent Systems with JADE, p. 300. Wiley, New York (2007)

    Book  Google Scholar 

  3. Bibimoune, M., Elghazel, H., Aussem, A.: An empirical comparison of supervised ensemble learning approaches. In: European Conference on Machine Learning, ECMLPKDD (2013)

    Google Scholar 

  4. Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Oxford (1995)

    MATH  Google Scholar 

  5. Bordini, R.H., Hubner, J.F., Woolridge, M.: Programming Multiagent Systems in AgentSpeak using Jason, p. 292. Wiley, Sussex (2007)

    Book  Google Scholar 

  6. Bradshaw, J.M.: An introduction to software agents. In: Software Agents, pp. 3–46. MIT Press, Massachusetts (1997). ISBN:0-262-52234-9

    Google Scholar 

  7. Breiman, L.: Bagging predictors. Mach. Learn. 24, 123–140 (1996)

    MATH  Google Scholar 

  8. Brenner, W., Zarnekow, R., Wittig, H.: Intelligent Software Agents: Foundations and Applications. Springer, Berlin (1998)

    Book  MATH  Google Scholar 

  9. Calderón, J., López-Ortega, O., Castro-Espinoza, F.A.: A multi-agent ensemble of classifiers. In: Sidorov, G., Galicia-Haro, S.N. (eds.) MICAI 2015. LNCS, vol. 9413, pp. 499–508. Springer, Cham (2015). doi:10.1007/978-3-319-27060-9_41

    Chapter  Google Scholar 

  10. Cervantes, L., Lee, J.-S., Lee, J.: Agent-based approach to distributed ensemble learning of fuzzy ARTMAP classifiers. In: Nguyen, N.T., Grzech, A., Howlett, Robert J., Jain, Lakhmi C. (eds.) KES-AMSTA 2007. LNCS, vol. 4496, pp. 805–814. Springer, Heidelberg (2007). doi:10.1007/978-3-540-72830-6_84

    Chapter  Google Scholar 

  11. FIPA: Foundation for intelligent physical agents. Disponível em. http://www.fipa.org/. Acesso em: 06 mai. 2016

  12. Guermeur, Y., Paugam-Moisy, H., Gallinari, P.: Multivariate linear regression on classifier outputs: a capacity study. In: Niklasson, L., Bodén, M., Ziemke, T. (eds.) ICANN 98 Perspectives in Neural Computing, pp. 693–698. Springer, London (1998). doi:10.1007/978-1-4471-1599-1_106

    Google Scholar 

  13. Guelpedi, M.V.C., Ribeiro, C.H., Omar, N.: Utilização de Aprendizagem por Reforço para Modelagem Autônoma do Aprendiz em um Tutor Inteligente. Simpósio Brasileiro de Informática na Educação. UFRJ (2003)

    Google Scholar 

  14. Hansen, L., Salomon, P.: Neural network ensembles. IEEE Trans. Pattern Anal. Mach. Intell. 12, 993–1001 (1990)

    Article  Google Scholar 

  15. Haykin, S.S.: Redes Neurais, 2nd edn. Bookman, New York (2001)

    Google Scholar 

  16. Haykin, S.S.: Neural Networks: A Comprehensive Foundation, 3rd edn. Prentice Hall, Upple Saddle River (2007)

    MATH  Google Scholar 

  17. Helmy, T. et al.: Adaptive ensemble and hybrid models for classification of bioinformatics datasets. Trans. Fuzzy Neural Netw. Bioinform. (2012)

    Google Scholar 

  18. JADE: Java agent development framework. Disponível em: http://jade.tilab.com. Acesso em: 5 mai. 2016

  19. Kohavi, R.A.: Study of cross-validation and bootstrap for accuracy estimation and model selection. Int. Joint Conf. Artif. Intell. 14, 1137–1145 (1995)

    Google Scholar 

  20. Lima, C.A.M.: Comitê de Máquinas: Uma abordagem Unificada Empregando Máquinas de Vetores-Suporte. Tese. Campinas: Universidade Estadual de Campinas. Programa de Pós-Graduação em Engenharia Elétrica e de Computação (2004)

    Google Scholar 

  21. Magid, N., Giovanni, C., Bahri, P.: A methodology for the analysis and design of multiagent systems using JADE. Int. J. Comput. Syst. Sci. Eng. Spec. Issue Softw. Eng. for Multiagent Syst. (2006). Murdoch University, Austrália

    Google Scholar 

  22. Nilsson, N.J.: Learning Machines. McGraw-Hill, New York (1965)

    MATH  Google Scholar 

  23. Oza, N.C., Tumer, K.: Classifier ensembles: select real-world applications. Inf. Fusion 9, 4–20 (2008)

    Article  Google Scholar 

  24. Perrone, M.P., Cooper, L.N.: When networks disagree: ensemble methods for hybrid neural networks. In: R. J

    Google Scholar 

  25. Rezende, S.O.: Sistemas Inteligentes: Fundamentos e aplicações, Barueri, São Paulo (2003)

    Google Scholar 

  26. Russel, S.J., Norvig, P.: Inteligência Artificial: A Modern Approach, p. 649. Prentice Hall, Upper Saddle River (2010)

    Google Scholar 

  27. Sacile, R., Paolucci, M.: Agent-based manufacturing and control systems, p. 288. CRC Press LLC, Flórida (2005)

    MATH  Google Scholar 

  28. Schapire, R.E.: The strength of weak learnability. Mach. Learn. 5(2), 197–227 (1990). Kluwer Academic Publishers, Boston

    Google Scholar 

  29. Seewald, A.K.: How to make stacking better and faster while also taking care of an unknown weakness. In: Proceedings of the Nineteenth International Conference on Machine Learning, pp. 554–561. Morgan Kaufmann Publishers (2002)

    Google Scholar 

  30. Silva, T.C.: Aprendizado de máquina em redes complexas: modelagem, análise e aplicações. Tese. São Carlos: Universidade de São Paulo –USP. Instituto de Ciências Matemáticas e de Computação (2012)

    Google Scholar 

  31. Sutton, R., Barto, A.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)

    Google Scholar 

  32. Junior, A.U., Silveira, R.A.: HIPS: Um Framework para Escalonamento Distribuído de Processos em Sistemas de Produção Utilizando Sistemas Multiagente. Avances en sistemas e informatica 7, 7–15 (2010)

    Google Scholar 

  33. Junior, A.U., de Freitas Filho, P.J., Silveira, R.A.: E-HIPS: an extention of the framework HIPS for stagger of distributed process in production systems based on multiagent systems and memetic algorithms. In: Sidorov, G., Galicia-Haro, S.N. (eds.) MICAI 2015. LNCS, vol. 9413, pp. 413–430. Springer, Cham (2015). doi:10.1007/978-3-319-27060-9_34

    Chapter  Google Scholar 

  34. Villar, S.O.: Ensemble case-based learning for multi-agent systems. Doctoral Thesis, Universitat Autonoma de Barcelona (2005)

    Google Scholar 

  35. Vrba, P.: JAVA-based agent platform evaluation. In: Mařík, V., McFarlane, D., Valckenaers, P. (eds.) HoloMAS 2003. LNCS, vol. 2744, pp. 47–58. Springer, Heidelberg (2003). doi:10.1007/978-3-540-45185-3_5

    Chapter  Google Scholar 

  36. Watking, C.J.C.H., Dayan, P.: Q-leaning. Mach. Learn. 8(3/4), 279–292 (1992)

    Article  Google Scholar 

  37. Wolpert, D.: The lack of a priori distinctions between learning algorithms. Neural Comput. 8, 1341–1390 (1996)

    Article  Google Scholar 

  38. Wozniack, M., Grana, M., Corchado, E.: A survey of multiple classifier systems as hybrid systems. Inf. Fusion 16, 3–17 (2014). Elsevier

    Article  Google Scholar 

  39. Yang, B., Braeuning, A., Johnson, K.R., Shi, Y.: General characteristics of temperature variation in China during the last two millennia. Geophys. Res. Lett. 29, 381–384 (2002)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Postgraduate Program in Computer Science - PPGCC, Federal University of Santa Catarina - UFSC, Florianópolis, Brazil

    Arnoldo Uber Junior, Paulo José de Freitas Filho, Ricardo Azambuja Silveira, Mariana Dehon Costa e Lima & Rodolfo Wilvert Reitz

Authors
  1. Arnoldo Uber Junior
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paulo José de Freitas Filho
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ricardo Azambuja Silveira
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mariana Dehon Costa e Lima
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rodolfo Wilvert Reitz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arnoldo Uber Junior .

Editor information

Editors and Affiliations

  1. Instituto Politécnico Nacional, Unidad Profesional Interdisciplinaria en Ingeniería y Tecnologías Avanzadas, México DF, Mexico

    Obdulia Pichardo-Lagunas

  2. INFOTEC Aguascalientes, Aguascalientes, Mexico

    Sabino Miranda-Jiménez

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Junior, A.U., de Freitas Filho, P.J., Azambuja Silveira, R., Costa e Lima, M.D., Reitz, R.W. (2017). iEnsemble: A Framework for Committee Machine Based on Multiagent Systems with Reinforcement Learning. In: Pichardo-Lagunas, O., Miranda-Jiménez, S. (eds) Advances in Soft Computing. MICAI 2016. Lecture Notes in Computer Science(), vol 10062. Springer, Cham. https://doi.org/10.1007/978-3-319-62428-0_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-62428-0_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-62427-3

  • Online ISBN: 978-3-319-62428-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation