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Implementation of Exploration in TONIC Using Non-stationary Volatile Multi-arm Bandits

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Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1048))

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Abstract

Target Oriented Network Intelligence Collection (TONIC) is a problem which deals with acquiring maximum number of profiles in the online social network so as to maximize the information about a given target through these profiles. The acquired profiles, also known as leads in this paper, are expected to contain information which is relevant to the target profile.In the past, TONIC problem has been solved by modelling it as a Volatile Multi-arm bandit problem with stationary reward distribution. The problem with this approach is that the underlying reward distribution in case of TONIC changes with each exploration which needs to be incorporated for making future acquisitions. The paper shows a successful solution to the TONIC problem by modelling it as Volatile Bandit problem with non-stationary reward distributions. It illustrates a new approach and compares it’s performance with other algorithms.

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References

  1. Stern, R., Samama, L., Puzis, R., Beja, T., Bnaya, Z.: TONIC: Target oriented network intelligence collection for the social web. In: 27th AAAI Conference on Artificial Intelligence, pp. 1184–1190 (2013)

    Google Scholar 

  2. Samama-Kachko, L.: Target Oriented network intelligence collection (TONIC) (2014)

    Google Scholar 

  3. Samama-kachko, L., Stern, R., Felner, A.: Extended Framework for Target Oriented Network Intelligence Collection. In: (SoCS), pp. 131–138 (2014)

    Google Scholar 

  4. Bnaya, Z., Puzis, R., Stern, R., Felner, A.: Bandit algorithms for social network queries. In: Proceedings-SocialCom/PASSAT/BigData/EconCom/BioMedCom 2013, 148153 (2013)

    Google Scholar 

  5. Chakrabarti, D., Kumar, R., Radlinski, F., Upfal, E.: Mortal multi-armed bandits. In: Neural Information Processing Systems, pp. 273–280 (2008)

    Google Scholar 

  6. Bnaya, Z., Puzis, R., Stern, R., Felner, A.: Volatile multi-armed bandits for guaranteed targeted social crawling. In: Late Breaking Papers at the Twenty-Seventh AAAI Conference on Artificial Intelligence, pp. 8–10 (2013)

    Google Scholar 

  7. Auer, P.: Using confidence bounds for Exploration Exploitation trade-offs. JMLR 3, 397–422 (2002)

    Google Scholar 

  8. Auer, P., Cesa-Bianchi, N., Fischer, P.: Mach. Learn. 47, 235 (2002). https://doi.org/10.1023/A:1013689704352

    Article  Google Scholar 

  9. Garivier, A., Moulines, E.: On upper-confidence bound policies for non-stationary bandit problems (2008). arXiv:0805.3415

  10. Kocsis, L., Szepesvri, C.: Discounted UCB. In: 2nd PASCAL Challenges Workshop, pp. 784–791 (2006)

    Google Scholar 

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Authors and Affiliations

  1. School of Computer Science and Engineering, VIT Vellore, Vellore, India

    Aditya Shaha & Dhruv Arya

  2. School of Information Technology and Engineering, VIT Vellore, Vellore, India

    B. K. Tripathy

Authors
  1. Aditya Shaha
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  2. Dhruv Arya
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  3. B. K. Tripathy
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Corresponding author

Correspondence to B. K. Tripathy .

Editor information

Editors and Affiliations

  1. Department of Mathematics, National Institute of Technology Silchar, Silchar, Assam, India

    Kedar Nath Das

  2. Department of Mathematics, South Asian University, New Delhi, Delhi, India

    Jagdish Chand Bansal

  3. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  4. Department of Mathematics, Faculty of Science, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

  5. School of Electrical Engineering, VIT University, Vellore, Tamil Nadu, India

    Ponnambalam Pathipooranam

  6. School of Electrical Engineering, VIT University, Vellore, Tamil Nadu, India

    Rani Chinnappa Naidu

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Shaha, A., Arya, D., Tripathy, B.K. (2020). Implementation of Exploration in TONIC Using Non-stationary Volatile Multi-arm Bandits. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1048. Springer, Singapore. https://doi.org/10.1007/978-981-15-0035-0_18

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