Abstract
Machine committees, as the name implies, are the union of more than one machine of learning in generating a solution to a given problem. During this process, several decisions must be taken to seek the generalization of the model and also to have the coordination to find a final solution at least satisfactory to the problem. At this point, agent theory plays a fundamental role, as it allows the agent’s autonomous decision-making, based on their experiences, as well as providing mechanisms to scale and distribute processing. Reinforcement learning is based on the existence of an external critic to the environment, which evaluates the action defined, but without explicitly indicating the correct action to be taken, in this way, allowing the training of agents in a gradual way and assisting in learning. This learning process can be accelerated by making use of heuristics on the problem domain. In this way, this article proposes a machine committee model based on multiagents system and learning by multi-person reinforcement accelerated by heuristics, describing the experiments performed and the results obtained.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aranibar, D.B.: Aprendizado por Reforço com Valores de Influência em SMA. Tese de Doutorado, Natal. Universidade Federal do Rio Grande do Norte (2009)
Bianchi, R., Martins, M., Ribeiro, C., Costa, A.: Heuristically-accelerated multiagent reinforcement learning. IEEE Trans. Cybern. 44(2), 252–265 (2014)
Blaser, R., Fryzlewicz, P.: Random rotation ensembles. J. Mach. Learn. Res. 17, 1–26 (2016)
Busoniu, L., Babuska, R., De Schutter, B.: A comprehensive survey of multiagent reinforcement learning. IEEE Trans. Syst. Man Cybern. C Appl. Rev. 38(2), 156–172 (2008)
Cervantes, L.J., Lee, S.: Agent-based approach to distributed ensemble learning. Agent and multi-agent systems: technologies and applications. In: First KES International Symposium, pp. 805–814 (2007)
Hansen, L., Salomon, P.: Neural network ensembles. IEEE Trans. Pattern Anal. Mach. Intell. 121, 993–1001 (1990)
He, H., et al.: Opponent modeling in deep reinforcement learning. In: Proceedings of the 33rd International Conference on Machine Learning, ICML 2016, vol. 48, pp. 1804–1813 (2016)
Helmy, T., et al.: Adaptive ensemble and hybrid models for classification of bioinformatics datasets. Trans. Fuzzy Neural Netw. Bioinform. 3, 20–29 (2012)
Hsu, K.-W.: A theoretical analysis of why hybrid ensembles works. Comput. Intell. Neurosci. 2017, 1–12 (2017)
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)
Lima, M.D.C., Nassar, S.M., de Freitas Filho, P.J.: Simulation of oil drilling time series using Monte Carlo and Bayesian Networks. In: 2015 Winter Simulation Conference (WSC), 2015, Huntington Beach, CA, pp. 1195–1205. WSC Abstracts (2015)
Lima Junior, F.C.: Algoritmo Q-Learning como Estratégia de Exploração/Explotação para as metaheurísticas GRASP e AG. Tese. Natal. Universidade Federal do Rio Grande do Norte (2009)
Mazzutti, T., Roisenberg, M., de Freitas Filho, P.J.: INFGMN—incremental neuro-fuzzy Gaussian mixture network. Expert Syst. Appl. 89, 160–178 (2017)
Mendoza, L.A.F: Coordenação Inteligente para Multiagentes baseados em Modelo Neuro-Fuzzy Hierárquicos com Aprendizado por Reforço. Tese. Pontífica Universidade Católica. Programa de Pós-Graduação Engenharia Elétrica, Rio de Janeiro (2013)
Nilsson, N.J.: Learning Machines. McGraw-Hill, New York (1965)
Oliveira, R.T.A., et al.: Copulas-based ensemble of artificial neural networks for forecasting real world time series. In: IEEE World Congress on Computational Intelligence – WCCI (2016)
Oliveira, R.T.A., et al.: Copulas-based time series combined forecasters. J. Inf. Sci. Int. J. 376, 110–124 (2017)
Perrone, M.P., Cooper, L.N.: When networks disagree: ensemble methods for hybrid neural networks, pp. 126--142. Chapman and Hall (1993)
Ravishankar, N.R., Vijayakumar, M.V.: Reinforcement learning algorithms: survey and classification. Indian J. Sci. Technol. 10, 1395–1401 (2017)
Russell, S.J., Norvig, P.: Inteligência Artificial: A Modern Approach. Prentice Hall, Upper Saddle River (2010)
Silva, T.C.: Aprendizado de máquina em redes complexas: modelagem, análise e aplicações. Tese. Universidade de São Paulo–USP. Instituto de Ciências Matemáticas e de Computação, São Carlos (2012)
Soares, S.G.: Ensemble Learning Methodologies for Soft Sensor Development in Industrial Processes. Tese. Universidade de Coimbra. Departamento de Engenharia Electrotécnica e de Computadores, Coimbra (2015)
Uber Junior, A., 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, 1st edn. LNCS, vol. 9413, pp. 413–430. Springer International Publishing (2015)
Uber Junior, A., de Freitas Filho, P.J., Silveira, R.A., Costa e Lima, M.D., Reitz, R.W.: iEnsemble: a framework for committee machine based on multiagent systems with reinforcement learning, 1st edn. LNCS, vol. 10062, pp. 59–70. Springer International Publishing (2017)
UCI: Machine Learning Repository. Disponível em: https://archive.ics.uci.edu/ml/index.php. Acesso em 12 Jan 2018
Villar, S.O.: Ensemble case-based learning for multi-agent systems. Doctoral thesis, Universitat Autonoma de Barcelona (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Uber Junior, A., de Freitas Filho, P.J., Silveira, R.A., Mueloschat, J. (2019). iEnsemble2: Committee Machine Model-Based on Heuristically-Accelerated Multiagent Reinforcement Learning. In: Barolli, L., Javaid, N., Ikeda, M., Takizawa, M. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2018. Advances in Intelligent Systems and Computing, vol 772. Springer, Cham. https://doi.org/10.1007/978-3-319-93659-8_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-93659-8_32
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-93658-1
Online ISBN: 978-3-319-93659-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)