Abstract
Traditional Learning Automata (LA) work with the understanding that the actions are chosen purely based on the “state” in which the machine is. This modus operandus completely ignores any estimation of the Random Environment’s (RE’s) (specified as \(\mathbb {E}\)) reward/penalty probabilities. To take these into consideration, Estimator/Pursuit LA utilize “cheap” estimates of the Environment’s reward probabilities to make them converge by an order of magnitude faster. This concept is quite simply the following: Inexpensive estimates of the reward probabilities can be used to rank the actions. Thereafter, when the action probability vector has to be updated, it is done not on the basis of the Environment’s response alone, but also based on the ranking of these estimates. While this phenomenon has been utilized in the field of LA, until recently, it has not been incorporated into solutions that solve partitioning problems. In this paper (The second author gratefully acknowledges the partial support of NSERC, the Natural Sciences and Engineering Council of Canada), we will submit a complete survey of how the “Pursuit” learning paradigm can be and has been used in Object Partitioning. The results demonstrate that incorporating this paradigm can hasten the partitioning by a order of magnitude.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
The bibliography in this paper is necessarily limited. The majority of the present results very briefly summarize the results in the Ph.D. thesis of the First Author.
- 2.
To be consistent with the terminology of LA, we use the terms “action”, “class” and “group” synonymously.
- 3.
The OMA’s algorithms/figures are in [11], and omitted here in the interest of space.
References
Godsil, C., Royle, G.F.: Algebraic Graph Theory, vol. 207. Springer, New York (2013)
Biggs, N.: Algebraic Graph Theory. Cambridge University Press, Cambridge (1993)
Fayyoumi, E., Oommen, B.J.: Achieving microaggregation for secure statistical databases using fixed-structure partitioning-based learning automata. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 39(5), 1192–1205 (2009)
Freuder, E.C.: The object partition problem. Vision Flash, WP-(4) (1971)
Gale, W., Das, S., Yu, C.T.: Improvements to an algorithm for equipartitioning. IEEE Trans. Comput. 39(5), 706–710 (1990)
Jobava, A.: Intelligent traffic-aware consolidation of virtual machines in a data center. Master’s thesis, University of Oslo (2015)
Lanctot, J.K., Oommen, B.J.: Discretized estimator learning automata. IEEE Trans. Syst. Man Cybern. 22(6), 1473–1483 (1992)
Mamaghani, A.S., Mahi, M., Meybodi, M.: A learning automaton based approach for data fragments allocation in distributed database systems. In: 2010 IEEE 10th International Conference on Computer and Information Technology (CIT), pp. 8–12. IEEE (2010)
Oommen, B.J., Ma, D.C.Y.: Stochastic automata solutions to the object partitioning problem. Carleton University, School of Computer Science (1986)
Oommen, B.J., Agache, M.: Continuous and discretized pursuit learning schemes: various algorithms and their comparison. IEEE Trans. Syst. Man Cybern. Part B: Cybern. 31(3), 277–287 (2001)
Shirvani, A.: Novel solutions and applications of the object partitioning problem. Ph.D. thesis, Carleton University, Ottawa, Canada (2018)
Yazidi, A., Granmo, O.C., Oommen, B.J.: Service selection in stochastic environments: a learning-automaton based solution. Appl. Intell. 36(3), 617–637 (2012)
Amer, A., Oommen, B.J.: A novel framework for self-organizing lists in environments with locality of reference: lists-on-lists. Comput. J. 50(2), 186–196 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Shirvani, A., Oommen, B.J. (2019). The Power of the “Pursuit” Learning Paradigm in the Partitioning of Data. In: Macintyre, J., Iliadis, L., Maglogiannis, I., Jayne, C. (eds) Engineering Applications of Neural Networks. EANN 2019. Communications in Computer and Information Science, vol 1000. Springer, Cham. https://doi.org/10.1007/978-3-030-20257-6_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-20257-6_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-20256-9
Online ISBN: 978-3-030-20257-6
eBook Packages: Computer ScienceComputer Science (R0)