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Aggregating Preferences Represented by Conditional Preference Networks

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Algorithmic Decision Theory (ADT 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13023))

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Abstract

This paper focuses on the task of aggregating preference orders over combinatorial domains, where both the individual and the aggregate preference orders are represented as Conditional Preference Networks (CP-nets). We propose intuitive objective functions for finding an optimal aggregate CP-net, as well as corresponding optimal efficient aggregation algorithms for inputs with certain structural properties.

This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC).

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Notes

  1. 1.

    Completeness is a limiting condition to be defined in Sect. 3.

References

  1. Airiau, S., Endriss, U., Grandi, U., Porello, D., Uckelman, J.: Aggregating dependency graphs into voting agendas in multi-issue elections. In: IJCAI (2011)

    Google Scholar 

  2. Bachmaier, C., Brandenburg, F., Gleißner, A., Hofmeier, A.: On the hardness of maximum rank aggregation problems. J. Discrete Alg. 31, 2–13 (2015)

    Google Scholar 

  3. Bigot, D., Zanuttini, B., Fargier, H., Mengin, J.: Probabilistic conditional preference networks. arXiv (2013)

    Google Scholar 

  4. Booth, R., Chevaleyre, Y., Lang, J., Mengin, J., Sombattheera, C.: Learning conditionally lexicographic preference relations. In: ECAI, pp. 269–274 (2010)

    Google Scholar 

  5. Boutilier, C., Brafman, R., Domshlak, C., Hoos, H., Poole, D.: CP-nets: a tool for representing and reasoning with conditional ceteris paribus preference statements. J. Artif. Intell. Res. 21, 135–191 (2004)

    Google Scholar 

  6. Brafman, R., Domshlak, C., Shimony, S.: On graphical modeling of preference and importance. J. Artif. Intell. Res. 25, 389–424 (2006)

    Google Scholar 

  7. Brandt, F., Conitzer, V., Endriss, U., Lang, J., Procaccia, A.: Handbook of Computational Social Choice. Cambridge University Press, Cambridge (2016)

    Google Scholar 

  8. Conitzer, V., Lang, J., Xia, L.: Hypercubewise preference aggregation in multi-issue domains. In: IJCAI (2011)

    Google Scholar 

  9. Cornelio, C., Grandi, U., Goldsmith, J., Mattei, N., Rossi, F., Venable, K.: Reasoning with PCP-nets in a multi-agent context. In: AAMAS, pp. 969–977 (2015)

    Google Scholar 

  10. Dinu, L., Manea, F.: An efficient approach for the rank aggregation problem. Theor. Comput. Sci. 359(1–3), 455–461 (2006)

    Google Scholar 

  11. Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank aggregation methods for the web. In: TheWebConf, pp. 613–622. ACM (2001)

    Google Scholar 

  12. Fagin, R., Kumar, R., Mahdian, M., Sivakumar, D., Vee, E.: Comparing partial rankings. SIAM J. Discrete Math. 20(3), 628–648 (2006)

    Google Scholar 

  13. Grandi, U., Luo, H., Maudet, N., Rossi, F.: Aggregating CP-nets with unfeasible outcomes. In: O’Sullivan, B. (ed.) CP 2014. LNCS, vol. 8656, pp. 366–381. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10428-7_28

  14. Koriche, F., Zanuttini, B.: Learning conditional preference networks. Artif. Intell. 174(11), 685–703 (2010)

    Google Scholar 

  15. Lang, J.: Vote and aggregation in combinatorial domains with structured preferences. In: IJCAI, vol. 7, pp. 1366–1371 (2007)

    Google Scholar 

  16. Lang, J., Xia, L.: Sequential composition of voting rules in multi-issue domains. Math. Soc. Sci. 57(3), 304–324 (2009)

    Google Scholar 

  17. Li, M., Vo, Q., Kowalczyk, R.: Majority-rule-based preference aggregation on multi-attribute domains with CP-nets. In: AAMAS, pp. 659–666 (2011)

    Google Scholar 

  18. Loreggia, A., Mattei, N., Rossi, F., Venable, K.: A notion of distance between CP-nets. In: AAMAS, pp. 955–963 (2018)

    Google Scholar 

  19. Lukasiewicz, T., Malizia, E.: Complexity results for preference aggregation over (m) CP-nets: Pareto and majority voting. Artif. Intell. 272, 101–142 (2019)

    Google Scholar 

  20. Rossi, F., Venable, K., Walsh, T.: mCP nets: representing and reasoning with preferences of multiple agents. In: AAAI, vol. 4, pp. 729–734 (2004)

    Google Scholar 

  21. Sculley, D.: Rank aggregation for similar items. In: Proceedings of the 2007 SIAM International Conference on Data Mining, pp. 587–592. SIAM (2007)

    Google Scholar 

  22. Xia, L., Conitzer, V., Lang, J.: Voting on multiattribute domains with cyclic preferential dependencies. In: AAAI, vol. 8, pp. 202–207 (2008)

    Google Scholar 

  23. Xia, L., Lang, J., Ying, M.: Sequential voting rules and multiple elections paradoxes. In: Proceedings of TARK, pp. 279–288 (2007)

    Google Scholar 

  24. Xia, L., Lang, J., Ying, M.: Strongly decomposable voting rules on multiattribute domains. In: AAAI, vol. 7, pp. 776–781 (2007)

    Google Scholar 

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Ali, A.M.H., Hamilton, H.J., Rayner, E., Yang, B., Zilles, S. (2021). Aggregating Preferences Represented by Conditional Preference Networks. In: Fotakis, D., Ríos Insua, D. (eds) Algorithmic Decision Theory. ADT 2021. Lecture Notes in Computer Science(), vol 13023. Springer, Cham. https://doi.org/10.1007/978-3-030-87756-9_1

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  • DOI: https://doi.org/10.1007/978-3-030-87756-9_1

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