Advertisement

A Generic Framework to Include Belief Functions in Preference Handling for Multi-criteria Decision

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10369)

Abstract

Modelling the preferences of a decision maker about multi-criteria alternatives usually starts by collecting preference information, then used to fit a model issued from a set of hypothesis (weighted average, CP-net). This can lead to inconsistencies, due to inaccurate information provided by the decision maker or to a poor choice of hypothesis set. We propose to quantify and resolve such inconsistencies, by allowing the decision maker to express her/his certainty about the provided preferential information in the form of belief functions.

Keywords

Decision Maker Partial Order Maximal Element Preference Model Information Item 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Benabbou, N., Perny, P., Viappiani, P.: Incremental elicitation of choquet capacities for multicriteria decision making. In: Proceedings of the Twenty-First European Conference on Artificial Intelligence, pp. 87–92. IOS Press (2014)Google Scholar
  2. 2.
    Beynon, M.J.: Understanding local ignorance and non-specificity within the DS/AHP method of multi-criteria decision making. Eur. J. Oper. Res. 163(2), 403–417 (2005)MathSciNetCrossRefMATHGoogle Scholar
  3. 3.
    Boujelben, M.A., De Smet, Y., Frikha, A., Chabchoub, H.: A ranking model in uncertain, imprecise and multi-experts contexts: the application of evidence theory. Int. J. Approximate Reasoning 52(8), 1171–1194 (2011)MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    Boutilier, C., Brafman, R.I., Domshlak, C., Hoos, H.H., Poole, D.: CP-nets: a tool for representing and reasoning with conditional ceteris paribus preference statements. J. Artif. Intell. Res. (JAIR) 21, 135–191 (2004)MathSciNetMATHGoogle Scholar
  5. 5.
    Destercke, S.: A pairwise label ranking method with imprecise scores and partial predictions. In: Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2013, Prague, Czech Republic, 23–27 September, Proceedings, Part II, pp. 112–127 (2013)Google Scholar
  6. 6.
    Grabisch, M., Kojadinovic, I., Meyer, P.: A review of methods for capacity identification in Choquet integral based multi-attribute utility theory: applications of the Kappalab R package. Eur. J. Oper. Res. 186(2), 766–785 (2008)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Masson, M., Destercke, S., Denoeux, T.: Modelling and predicting partial orders from pairwise belief functions. Soft Comput. 20(3), 939–950 (2016)CrossRefGoogle Scholar
  8. 8.
    Pichon, F., Destercke, S., Burger, T.: A consistency-specificity trade-off to select source behavior in information fusion. IEEE Trans. Cybern. 45(4), 598–609 (2015)CrossRefGoogle Scholar
  9. 9.
    Rademaker, M., De Baets, B.: A threshold for majority in the context of aggregating partial order relations. In: 2010 IEEE International Conference on Fuzzy Systems (FUZZ), pp. 1–4. IEEE (2010)Google Scholar
  10. 10.
    Smets, P.: Analyzing the combination of conflicting belief functions. Inf. Fusion 8, 387–412 (2006)CrossRefGoogle Scholar
  11. 11.
    Viappiani, P., Boutilier, C.: Optimal bayesian recommendation sets and myopically optimal choice query sets. In: Advances in Neural Information Processing Systems, pp. 2352–2360 (2010)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Sorbonne Université, UMR CNRS 7253 Heudiasyc, Université de Technologie de Compiègne CS 60319Compiègne CedexFrance

Personalised recommendations