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The object migration automata: its field, scope, applications, and future research challenges

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Abstract

Partitioning, in and of itself, is an NP-hard problem. Prior to the Artificial Intelligence (AI)-based solutions, it was solved in the 1970s by optimization-based strategies. However, AI-based solutions appeared in the 1980s in a pioneering way, by using a Learning Automaton (LA)-motivated strategy known as the so-called Object Migrating Automaton (OMA). Although the OMA and its derivatives have been used in numerous applications since then, the basic kernel has remained the same. Because the number of possible partitions in a partitioning problem can be combinatorially exponential and the underlying tasks are NP-hard, the most advanced OMA algorithms could, until recently, only solve issues involving equally sized groups. Due to our recent innovations cited in the body of this paper, the enhanced OMA now also handles non-equally sized groups. Earlier, we had presented in Omslandseter (Pattern Anal Appl, 2023), a comprehensive survey of the state-of-the-art enhancements of the best-known OMA. We believe that these results will be the benchmark for a few decades and that it will be very hard to beat these results. This is a companion paper, intended to augment the contents of Omslandseter (Pattern Anal Appl, 2023). In this paper, we first discuss the OMA’s prior applications, its historical and current innovations, and the OMA-based algorithms’ relevance to societal needs. We also provide well-specified guidelines for future researchers so that they can use them for unresolved tasks, and also develop further advancements.

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Data availability

Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

Notes

  1. The paper referred to was among a new series of papers from selected “world renowned scientists.” The first author was honored to be included in this “class.”

  2. For more detailed descriptions of different algorithms in the OMA paradigm, the reader is referred to [3,4,5].

  3. Another pursuit-based version, based on the “Vanilla” OMA, the Pursuit OMA (POMA) can be found in [8].

  4. For the EOMA, the convergence criterion also includes the second innermost action. Thus, all objects need to be located in the innermost or second innermost states for the algorithm to be considered to have “converged.”

  5. More detailed explanation can be found in [1, 5].

References

  1. Omslandseter RO, Jiao L, John Oommen B (2023) Learning automata-based partitioning algorithms for Stochastic grouping problems with non-equal partition sizes. Pattern Anal Appl

  2. Glimsdal S, Granmo O-C (2014)“A Novel Bayesian Network Based Scheme for Finding the Optimal Solution to Stochastic Online Equi-Partitioning Problems,” 2014 13th International Conference on Machine Learning and Applications, pp. 594–599

  3. Bisong E (2018) “On Designing Adaptive Data Structures with Adaptive Data “Sub”-Structures,” Master’s thesis, Carleton University, Ottawa

  4. Shirvani A (2018) “Novel solutions and applications of the object partitioning problem,” Ph.D. thesis, Carleton University, Ottawa

  5. Omslandseter RO (2020) “Learning automata-based object partitioning with pre-specified cardinalities,” Master’s thesis, University of Agder, Grimstad

  6. John Oommen B, Ma DCY (1992) Stochastic automata solutions to the object partitioning problem. Comput J 35:A105–A120

    Google Scholar 

  7. Gale W, Das S, Yu CT (1990) Improvements to an algorithm for equipartitioning. IEEE Trans Comput 39:706–710

    Article  Google Scholar 

  8. Shirvani A, John Oommen B (2018) On enhancing the object migration automaton using the pursuit paradigm. J Comput Sci 24:329–342

    Article  MathSciNet  Google Scholar 

  9. Shirvani A, John Oommen B (2017) “On utilizing the pursuit paradigm to enhance the deadlock-preventing object migration automaton,” International Conference on New Trends in Computing Sciences (ICTCS), pp. 295–302

  10. Shirvani A, John Oommen B (2019) On enhancing the Deadlock-preventing object migration automaton using the pursuit paradigm. Pattern Anal Appl

  11. Shirvani A, John Oommen B (2018) On invoking transitivity to enhance the pursuit-oriented object migration automata. IEEE Access 6:21668–21681

    Article  Google Scholar 

  12. John Oommen B, Zgierski J (1993) A learning automaton solution to breaking substitution ciphers. IEEE Trans Pattern Anal Mach Intell 15:185–192

    Article  Google Scholar 

  13. John Oommen B, Zgierski JR (1993) Breaking substitution cyphers using stochastic automata. IEEE Trans Pattern Anal Mach Intell 15(2):185–192

    Article  Google Scholar 

  14. John Oommen B, Fothergill C (1992) The image examination and retrieval problem: a learning automaton-based solution. In: Proceedings ICARCV’92, International Conference on Automation, Robotics, and Computer Vision, IEEE

  15. John Oommen B, Fothergill C (1993) Fast learning automaton-based image examination and retrieval. Comput J 36(6):542–553

    Article  Google Scholar 

  16. Fayyoumi E, John Oommen B (2006) A fixed structure learning automaton micro-aggregation technique for secure statistical databases. International Conference on Privacy in Statistical Databases, Springer, pp. 114–128

  17. Fayyoumi E, John Oommen B (2009) Achieving microaggregation for secure statistical databases using fixed-structure partitioning-based learning automata. IEEE Trans Syst Man Cybern Part B (Cybernetics), 39(5):1192–1205

  18. Mamaghani AS, Mahi M, Meybodi MR (2010) A learning automaton based approach for data fragments allocation in distributed database systems. 2010 10th IEEE International Conference on Computer and Information Technology, pp. 8–12

  19. Yazidi A, Granmo O-C, John Oommen B (2012) Service selection in stochastic environments: a learning-automaton based solution. Appl Intell 36(3):617–637

    Article  Google Scholar 

  20. Jobava A, Yazidi A, John Oommen B, Begnum K (2016) Achieving intelligent traffic-aware consolidation of virtual machines in a data center using learning automata,” 2016 8th IFIP International Conference on New Technologies, Mobility and Security (NTMS), pp. 1–5

  21. Ung FM (2015) Towards efficient and cost-effective live migrations of virtual machines,” Master’s thesis, Carleton University, Ottawa

  22. Ekaba OB, John Oommen B (2020) Optimizing self-organizing Lists-on-Lists Using Transitivity and Pursuit-Enhanced Object Partitioning,” in Artificial Intelligence Applications and Innovations, (I. Maglogiannis, L. Iliadis, and E. Pimenidis, eds.), IFIP Advances in Information and Communication Technology, (Cham), pp. 227–240, Springer International Publishing

  23. Omslandseter RO, Jiao L, Liu Y, John Oommen B (2022)“User Grouping and Power Allocation in NOMA Systems: A Novel Semi Supervised Reinforcement Learning-based Solution,” Pattern Analysis and Applications,

  24. Lannelongue L, Grealey J, Inouye M (2021) Green algorithms: quantifying the carbon footprint of computation. Adv Sci 8(12)

  25. Henderson P, Hu J, Romoff J, Brunskill E, Jurafsky D, Pineau J (2022)Towards the systematic reporting of the energy and carbon footprints of machine learning. J Mach Learn Res 21:248:10039–248:10081

  26. The Directorate-General for Research and Innovation (The European Commission), A. Renda, S. Schwaag Serger, D. Tataj, A. Morlet, D. Isaksson, F. Martins, M. Mir Roca, C. Hidalgo, A. Huang, S. Dixson-Decleve, P.-A. Balland, F. Bria, C. Charveriat, K. Dunlop, and E. Giovannini, “Industry 5.0, a Transformative Vision for Europe: Governing Systemic Transformations Towards a Sustainable Industry,” LU: Publications Office of the European Union (2021)

  27. Allen J, Howland B, Mobius M, Rothschild D, Watts DJ (2020) Evaluating the fake news problem at the scale of the information ecosystem. Sci Adv 6

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Authors and Affiliations

  1. School of Computer Science, Carleton University, Ottawa, K1S 5B6, Canada

    B. John Oommen

  2. University of Agder, Grimstad, Norway

    B. John Oommen

  3. Department of Information and Communication Technology, University of Agder, Jon Lilletuns vei, 4879, Grimstad, Norway

    Rebekka Olsson Omslandseter & Lei Jiao

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  1. B. John Oommen
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  2. Rebekka Olsson Omslandseter
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  3. Lei Jiao
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Correspondence to B. John Oommen.

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Oommen, B.J., Omslandseter, R.O. & Jiao, L. The object migration automata: its field, scope, applications, and future research challenges. Pattern Anal Applic 26, 917–928 (2023). https://doi.org/10.1007/s10044-023-01163-x

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