Abstract
Learning behaviors of the hierarchical structure learning automata (HSLA) with the three representative algorithms under the nonstationary multiteacher environments are considered. Several computer simulations confirm the effectiveness of the newly developed relative reward strength algorithm (NRRSA).
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References
Varshavskii, V.I., Vorontsova, I.P.: On the behavior of stochastic automata with variable structure. Automation and Remote Control 24, 327–333 (1963)
Baba, N.: New Topics in Learning Automata Theory and Applications. Springer, Heidelberg (1985)
Narendra, K.S., Thathachar, M.A.L.: Learning Automata: An Introduction. Prentice-Hall, Englewood Cliffs, NJ (1989)
Thathachar, M.A.L., Ramakrishnan, K.R.: A hierarchical system of learning automata. IEEE Trans. Syst. Man, Cybern. SMC-11, 236–241 (1981)
Baba, N., Sawaragi, Y.: On the learning behavior of stochastic automata under a nonstationary random environment. IEEE Trans. Syst. Man, Cybern. SMC-5, 273–275 (1975)
Srikantakumar, P.R., Narendra, K.S.: A learning model for routing in telephone networks. SIAM Journal on Control and Optimization 20, 34–57 (1982)
Simha, R., Kurose, J.F.: Relative reward strength algorithms for learning automata. IEEE Trans. Syst. Man, Cybern. 19, 388–398 (1989)
Agache, M., Oommen, B.J.: Generalized pursuit learning scheme: New families of continuous and discretized learning automata. IEEE Trans. Syst. Man, Cybern. B 32, 738–749 (2002)
Baba, N., Mogami, Y.: A new learning algorithm for the hierarchical structure learning automata operating in the nonstationary S-model random environment. IEEE Trans. Syst. Man, Cybern. B 32, 750–758 (2002)
Papadimitriou, G.I., Sklira, M., Pomportsis, A.S.: [10] Papadimitriou, G. IEEE Trans. Syst. Man, Cybern. B 34, 246–254 (2004)
Baba, N., Mogami, Y.: A relative reward-strength algorithm for the hierarchical structure learning automata operating in the general nonstationary multiteache environment. IEEE Trans. Syst. Man, Cybern. B 36, 781–794 (2006)
Baba, N., Mogami, Y.: Hierarchical structure learning automata and their applications to the intelligent behavior of the mobile robots in a maze. In: Proceedings of the ISME2007, T04 Waseda Univ. Kitakyushu (2006)
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Baba, N., Mogami, Y. (2007). A Consideration on the Learning Performances of the Hierarchical Structure Learning Automata (HSLA) Operating in the General Nonstationary Multiteacher Environment. In: Apolloni, B., Howlett, R.J., Jain, L. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2007. Lecture Notes in Computer Science(), vol 4694. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74829-8_11
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DOI: https://doi.org/10.1007/978-3-540-74829-8_11
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