Abstract
A novel learning automata (LA) based cooperative student-team in tutorial-like system is presented in this paper. The students in our system are modeled using LA. The new philosophy of a student is that he acquires knowledge not only from teacher, but also from team-workers. The self-examination indicator makes it possible for students to evaluate his learning outcomes. The below normal learner adopts the collective intelligence to improve himself. Experiments demonstrate the proposed method’s convergence speed is comparable to the student-classroom interaction method [9] and even better when the environment becomes harder. Compared to the previous interaction method and the single operated student, our accuracy is significantly improved.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Yuan, W., Leung, H., Cheng, W., et al.: Optimizing Voting Rule for Cooperative Spectrum Sensing through Learning Automata. IEEE Trans. Vehicular Technology 60(7), 3253–3264 (2011)
Torkestani, J.A.: LAAP: A Learning Automata-based Adaptive Polling Scheme for Clustered Wireless Ad-hoc Networks. Wireless Personal Communications 69(2), 841–855 (2013)
Misra, S., Oommen, B.J., Yanamandra, S., et al.: Random Early Detection for Congestion Avoidance in Wired Networks: a Discretized Pursuit Learning-automata-like Solution. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 40(1), 66–76 (2010)
Horn, G., Oommen, B.J.: Solving Multiconstraint Assignment Problems Using Learning Automata. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 40(1), 6–18 (2010)
Thathachar, M.A.L., Oommen, B.J.: Discretized reward-inaction learning automata. Cybern. Inf. Sci. 2(1), 24–29 (1979)
Thathachar, M.A.L., Sastry, P.S.: A new approach to the design of reinforcement schemes for learning automata. IEEE Transactions on Systems, Man and Cybernetics 1(1), 168–175 (1985)
Papadimitriou, G.I., Sklira, M., Pomportsis, A.S.: A New Class of ε-optimal Learning Au-tomata. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 34(1), 246–254 (2004)
Zhang, X., Granmo, O.-C., Oommen, B.J.: The Bayesian Pursuit Algorithm: A New Family of Estimator Learning Automata. In: Mehrotra, K.G., Mohan, C.K., Oh, J.C., Varshney, P.K., Ali, M. (eds.) IEA/AIE 2011, Part II. LNCS, vol. 6704, pp. 522–531. Springer, Heidelberg (2011)
Oommen, B.J., Hashem, M.K.: Modeling a Student–classroom Interaction in a Tutorial-like System Using Learning Automata. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 40(1), 29–42 (2010)
Oommen, B.J., Hashem, M.K.: Modeling a Domain in a Tutorial-like System Using Learning Automata. Acta Cybern. 19(3), 635–653 (2010)
Agache, M., Oommen, B.J.: Generalized Pursuit Learning Schemes: New Families of Continuous and Discretized Learning Automata. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 32(6), 738–749 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Wang, Y., Jiang, W., Ma, Y., Ge, H., Jing, Y. (2014). Learning Automata Based Cooperative Student-Team in Tutorial-Like System. In: Huang, DS., Jo, KH., Wang, L. (eds) Intelligent Computing Methodologies. ICIC 2014. Lecture Notes in Computer Science(), vol 8589. Springer, Cham. https://doi.org/10.1007/978-3-319-09339-0_16
Download citation
DOI: https://doi.org/10.1007/978-3-319-09339-0_16
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09338-3
Online ISBN: 978-3-319-09339-0
eBook Packages: Computer ScienceComputer Science (R0)