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Case-Based Sample Generation Using Multi-Armed Bandits

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Case-Based Reasoning Research and Development (ICCBR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 14141))

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Abstract

A central problem in knowledge-based tasks is to provide a collection of reusable knowledge samples extracted from a textual corpus. Often, such corpora are structured into different documents or topics, respectively. The samples need to be proven for usability and adapted by a domain expert requiring a certain processing time for each sample taken. The goal is to achieve an optimal retrieval and adaptation success meeting the time budget of the domain expert. In this work, we formulate this task as a constrained multi-armed bandit model. We combine it with the model of a configurable data-driven case-based learning agent. A case study evaluates the theoretical considerations in a scenario of regulatory knowledge acquisition. Therefore, a data set is constructed out of a corpus of nuclear safety documents. We use the model to optimize the evaluation process of sample generation of adaptational knowledge. The corresponding knowledge graph has been created in an information extraction step by automatically identifying semantic concepts and their relations.

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References

  1. International Atomic Energy Association. https://www.iaea.org

  2. Alon, N., Cesa-Bianchi, N., Gentile, C., Mannor, S., Mansour, Y., Shamir, O.: Nonstochastic multi-armed bandits with graph-structured feedback. SIAM J. Comput. 46, 1785–1826 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  3. Auer, P.: Using confidence bounds for exploitation-exploration trade-offs. J. Mach. Learn. Res. 3, 397–422 (2002)

    MathSciNet  MATH  Google Scholar 

  4. Auer, P., Cesa-Bianchi, N., Freund, Y., Schapire, R.: The nonstochastic multiarmed bandit problem. SIAM J. Comput. 32, 48–77 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  5. Banko, M., Cafarella, M.J., Soderland, S., Broadhead, M., Etzioni, O.: Open information extraction from the web. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence, IJCAI 2007, pp. 2670–2676. Morgan Kaufmann Publishers Inc., San Francisco (2007)

    Google Scholar 

  6. Bengs, V., Hüllermeier, E.: Multi-armed bandits with censored consumption of resources. Mach. Learn. 112(1), 217–240 (2023). https://doi.org/10.1007/s10994-022-06271-z

    Article  MathSciNet  MATH  Google Scholar 

  7. Bergmann, R.: Experience Management. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-45759-3

    Book  MATH  Google Scholar 

  8. Brändle, F., Binz, M., Schulz, E.: Exploration beyond bandits, pp. 147–168. Cambridge University Press, Cambridge (2022)

    Google Scholar 

  9. Carlsson, E., Dubhashi, D.P., Johansson, F.D.: Thompson sampling for bandits with clustered arms. In: IJCAI International Joint Conference on Artificial Intelligence (2021)

    Google Scholar 

  10. Hartig, O.: Foundations of RDF* and SPARQL*: (an alternative approach to statement-level metadata in RDF). In: Alberto Mendelzon Workshop on Foundations of Data Management (2017)

    Google Scholar 

  11. Hearst, M.A.: Automatic acquisition of hyponyms from large text corpora. In: COLING 1992 Volume 2: The 15th International Conference on Computational Linguistics, pp. 539–545 (1992)

    Google Scholar 

  12. Hong, J., Kveton, B., Katariya, S., Zaheer, M., Ghavamzade, M.: Deep hierarchy in bandits. In: ICML International Conference on Machine Learning (2022)

    Google Scholar 

  13. Korger, A., Baumeister, J.: The SECCO ontology for the retrieval and generation of security concepts. In: Cox, M.T., Funk, P., Begum, S. (eds.) ICCBR 2018. LNCS (LNAI), vol. 11156, pp. 186–201. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01081-2_13

    Chapter  Google Scholar 

  14. Korger, A., Baumeister, J.: Case-based generation of regulatory documents and their semantic relatedness. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) FICC 2020. AISC, vol. 1130, pp. 91–110. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39442-4_9

    Chapter  Google Scholar 

  15. Kumar, S., Gao, H., Wang, C., Chang, K., Sundaram, H.: Hierarchical multi-armed bandits for discovering hidden populations. In: ASONAM 2019: International Conference on Advances in Social Networks Analysis and Mining, pp. 145–153, August 2019

    Google Scholar 

  16. Langford, J., Zhang, T.: The epoch-greedy algorithm for contextual multi-armed bandits. In: Proceedings of the 20th International Conference on Neural Information Processing Systems, NIPS 2007, pp. 817–824. Curran Associates Inc., Red Hook (2007)

    Google Scholar 

  17. Losada, D.E., Parapar, J., Barreiro, Á.: Multi-armed bandits for adjudicating documents in pooling-based evaluation of information retrieval systems. Inf. Process. Manag. 53, 1005–1025 (2017)

    Article  Google Scholar 

  18. Perotto, F.S., Verstaevel, N., Trabelsi, I., Vercouter, L.: Combining bandits and lexical analysis for document retrieval in a juridical corpora. In: Bramer, M., Ellis, R. (eds.) SGAI 2020. LNCS (LNAI), vol. 12498, pp. 317–330. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63799-6_24

    Chapter  Google Scholar 

  19. Racharak, T., Suntisrivaraporn, B., Tojo, S.: sim\(^\pi \): a concept similarity measure under an agent’s preferences in description logic ELH. In: 8th International Conference on Agents and Artificial Intelligence, pp. 480–487, January 2016

    Google Scholar 

  20. Rahman, M.M., Kutlu, M., Lease, M.: Constructing test collections using multi-armed bandits and active learning. In: The Web Conference, San Francisco, May 2019

    Google Scholar 

  21. Robbins, H.E.: Some aspects of the sequential design of experiments. Bull. Am. Math. Soc. 58, 527–535 (1952)

    Article  MathSciNet  MATH  Google Scholar 

  22. Schelling, T.C.: Dynamic models of segregation. J. Math. Sociol. 1(2), 143–186 (1971)

    Article  MATH  Google Scholar 

  23. Schulz, E., Franklin, N., Gershman, S.: Finding structure in multi-armed bandits. Cogn. Psychol. 119, 101261 (2020)

    Article  Google Scholar 

  24. Sen, R., et al.: Top-\(k\) extreme contextual bandits with arm hierarchy, February 2021

    Google Scholar 

  25. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, 2nd edn. The MIT Press, Cambridge (2018)

    MATH  Google Scholar 

  26. Thompson, W.R.: On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika 25, 285–94 (1933)

    Article  MATH  Google Scholar 

  27. Vaswani, A., et al.: Attention is all you need. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017)

    Google Scholar 

  28. W3C: SKOS Simple Knowledge Organization System Reference, August 2009. http://www.w3.org/TR/skos-reference

  29. Wood, D., Lanthaler, M., Cyganiak, R.: RDF 1.1 concepts and abstract syntax, February 2014. http://www.w3.org/TR/2014/REC-rdf11-concepts-20140225/

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Authors and Affiliations

  1. University of Würzburg, Am Hubland, 97074, Würzburg, Germany

    Andreas Korger & Joachim Baumeister

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  1. Andreas Korger
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  2. Joachim Baumeister
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Correspondence to Joachim Baumeister .

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Editors and Affiliations

  1. Robert Gordon University, Aberdeen, UK

    Stewart Massie

  2. Indian Institute of Technology Madras, Chennai, Tamil Nadu, India

    Sutanu Chakraborti

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Korger, A., Baumeister, J. (2023). Case-Based Sample Generation Using Multi-Armed Bandits. In: Massie, S., Chakraborti, S. (eds) Case-Based Reasoning Research and Development. ICCBR 2023. Lecture Notes in Computer Science(), vol 14141. Springer, Cham. https://doi.org/10.1007/978-3-031-40177-0_8

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  • DOI: https://doi.org/10.1007/978-3-031-40177-0_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-40176-3

  • Online ISBN: 978-3-031-40177-0

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