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Intelligent University Library Information Systems to Support Students Efficient Learning

  • Laszlo Barna Iantovics
  • Corina Rotar
  • Elena Nechita
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11306)

Abstract

The extension of the actual university library information systems (ULISs), in order to offer intelligent support for the registered students, was identified - both by users and specialists - as a necessity. During the documentation process, performed by the students in the library, the need to formulate various issues for which they wish to find rapid answers frequently emerges. The students’ requests could be very diverse, ranging from simple questions to complex issues. Our paper introduces a type of complex, intelligent university library information system that we refer to as NextGenULIS. A NextGenULIS is able to intelligently support students in finding answers to the issues formulated within a network of students, teachers and other possible intelligent agents. Several novel related paradigms, such as hybridization of a library information system, specialization in providing support, and complexity hiding, are also proposed. NextGenULIS is briefly compared with a recently introduced ULIS called IntelligUnivLibSys.

Keywords

Intelligent library information system Computational intelligence in a library information system Intelligent agent Cooperative hybrid search 

Notes

Acknowledgment

The authors acknowledge the support of the project “Bacău and Lugano - Teaching Informatics for a Sustainable Society”, co-financed by a grant from Switzerland through the Swiss Contribution to the enlarged European Union.

This publication does not necessarily reflect the position of the Swiss government. The responsibility for its content lies entirely with the authors.

References

  1. 1.
    Hajduk, M., Sukop, M., Haun, M.: Cognitive Multi-agent Systems. Structures, Strategies and Applications to Mobile Robotics and Robosoccer. Studies in Systems, Decision and Control Series, vol. 138. Springer, Berlin (2019).  https://doi.org/10.1007/978-3-319-93687-1CrossRefGoogle Scholar
  2. 2.
    Weiss, G. (ed.): Multiagent Systems: A Modern Approach to Distributed Artificial Intelligence. MIT Press, Cambridge (2000)Google Scholar
  3. 3.
    Thelwall, M., Maflahi, N.: How important is computing technology for library and information science research? Libr. Inf. Sci. Res. 37(1), 42–50 (2015)CrossRefGoogle Scholar
  4. 4.
    Iantovics, L.B., Kovacs, L., Fekete, G.L.: Next generation university library information systems based on cooperative learning. New Rev. Inf. Netw. 21(2), 101–116 (2016)CrossRefGoogle Scholar
  5. 5.
    Raju, J.: Information professional or IT professional?: The knowledge and skills required by academic librarians in the digital library environment. Portal Libr. Acad. 17(4), 739–757 (2017)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Reddy, R.: The Universal Library: Intelligent agents and information on demand. In: Adam, N.R., Bhargava, B.K., Halem, M., Yesha, Y. (eds.) Digital Libraries Research and Technology Advances. ADL 1995. LNCS, vol. 1082, pp. 27–34. Springer, Heidelberg (1996).  https://doi.org/10.1007/BFb0024597CrossRefGoogle Scholar
  7. 7.
    Kaklauskas, A., Zavadskas, E., Babenskas, E., Seniut, M., Vlasenko, A., Plakys, V.: Intelligent library and tutoring system for brita in the PuBs project. In: Luo, Y. (ed.) Cooperative Design, Visualization, and Engineering. CDVE 2007. LNCS, vol. 4674, pp. 157–166. Springer, Heidelberg (2007).  https://doi.org/10.1007/978-3-540-74780-2CrossRefGoogle Scholar
  8. 8.
    Younis, M.I.: SLMS: a smart library management system based on an RFID technology. Int. J. Reason. Based Intell. Syst. 4(4), 186–191 (2012)Google Scholar
  9. 9.
    Pandey, J., Kazmi, S.I.A., Hayat, M.S., Ahmed, I.: A study on implementation of smart library systems using IoT. In: 2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS), pp. 193–197. IEEE Press (2017)Google Scholar
  10. 10.
    AlHamad, A.Q.M., AlHammadi, R.A.: Students’ perception of E-library system at Fujairah University. In: Auer, M., Langmann, R. (eds.) Smart Industry & Smart Education. REV 2018. LNNS, vol. 47, pp. 659–670. Springer, Cham (2019).  https://doi.org/10.1007/978-3-319-95678-7CrossRefGoogle Scholar
  11. 11.
    Mirjalili, S.: Evolutionary Algorithms and Neural Networks. Theory and Applications. Studies in Computational Intelligence, vol. 780. Springer, Cham (2019).  https://doi.org/10.1007/978-3-319-93025-1CrossRefzbMATHGoogle Scholar
  12. 12.
    Mahmoud, Magdi S.: Fuzzy Control, Estimation and Diagnosis. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-54954-5CrossRefzbMATHGoogle Scholar
  13. 13.
    Liu, G.: The application of intelligent agents in libraries: a survey. Progr. Electron. Libr. Inf. Syst. 45(1), 78–97 (2011)Google Scholar
  14. 14.
    Herron, J.: Intelligent agents for the library. J. Electron. Resour. Med. Libr. 14(3–4), 139–144 (2017)CrossRefGoogle Scholar
  15. 15.
    Beemer, J., Spoon, K., He, L., Fan, J., Levine, R.A.: Ensemble learning for estimating individualized treatment effects in student success studies. Int. J. Artif. Intell. Educ. 28(3), 315–335 (2018)CrossRefGoogle Scholar
  16. 16.
    Dermeval, D., Paiva, R., Bittencourt, I.I., Vassileva, J., Borges, D.: Authoring tools for designing intelligent tutoring systems: a systematic review of the literature. Int. J. Artif. Intell. Educ. 28(3), 336–384 (2018)CrossRefGoogle Scholar
  17. 17.
    Dent, V.F.: Intelligent agent concepts in the modern library. Libr. Hi Tech. 25(1), 108–125 (2007)CrossRefGoogle Scholar
  18. 18.
    Sandholm, T.W.: An implementation of the contract net protocol based on marginal cost calculations. In: 1993 National Conference on Artificial Intelligence, pp. 295–308. AAAI Press, California (1993)Google Scholar
  19. 19.
    Smith, R.G.: The contract net protocol: high-level communication and control in a distributed problem solver. IEEE Trans. Comput. 29(12), 1104–1113 (1980)CrossRefGoogle Scholar
  20. 20.
    Wooldridge, M., Laurence, M.: The complexity of contract negotiation. Artif. Intell. 164, 23–46 (2005)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Wang, Z., Wang, S.: Distributed collaborative control model based on improved contract net. In: Xhafa, F., Patnaik, S., Zomaya, A. (eds.) Advances in Intelligent Systems and Interactive Applications, IISA 2017. AISC, vol. 686, pp. 779–784. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-69096-4CrossRefGoogle Scholar
  22. 22.
    Gupta, A.K., Gallasch, G.E.: Equivalence class verification of the contract net protocol-extension. Int. J. Softw. Tools Technol. Transf. 18(6), 685–706 (2016)CrossRefGoogle Scholar
  23. 23.
    Xie, Y., Wang, H.: A group cooperative decision support system based on extended contract net. Group Decis. Negot. 23(5), 1191–1217 (2014)CrossRefGoogle Scholar
  24. 24.
    Macau, E.E.N. (ed.): A Mathematical Modeling Approach from Nonlinear Dynamics to Complex Systems. Nonlinear Systems and Complexity, vol. 22. Springer, Cham (2019).  https://doi.org/10.1007/978-3-319-78512-7CrossRefzbMATHGoogle Scholar
  25. 25.
    Iantovics, L.B., Rotar, C., Niazi, M.A.: MetrIntPair—a novel accurate metric for the comparison of two cooperative multiagent systems intelligence based on paired intelligence measurements. Int. J. Intell. Syst. 33(3), 463–486 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Laszlo Barna Iantovics
    • 1
  • Corina Rotar
    • 2
  • Elena Nechita
    • 3
  1. 1.University of Medicine, Pharmacy, Sciences and Technology of Targu MuresTirgu MuresRomania
  2. 2.“1 Decembrie 1918” UniversityAlba IuliaRomania
  3. 3.“Vasile Alecsandri” UniversityBacauRomania

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