Abstract
SRMP, which stands for “Simple Ranking with Multiple Profiles”, is a Multi-Criteria Decision Aiding model aiming to rank alternatives according to the preferences of a Decision Maker (DM) using reference criteria evaluations. Determining the preference parameters of SRMP can be tiring for the DM, who is often asked to compare several alternatives pairwisely during a preference elicitation process. It has been proposed in the literature to use an incremental elicitation process which selects informative pairs of alternatives which are submitted to the DM in sequence. The goal in such a process is to refine the SRMP model at each iteration, until a robust recommendation is determined, while limiting the cognitive effort of the DM. In this research, using a regret-based elicitation approach, we present a new heuristic for choosing the pairs of alternatives sequentially submitted for evaluation to the DM. We also provide a mixed-integer linear program for an efficient computation of regret values in practice. We limit our solution to the elicitation of the criteria weights, a subset of the SRMP model’s parameters, and we demonstrate that in this setting, the suggested heuristic outperforms previously examined query selection algorithms.
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References
Belahcène K, Mousseau V, Ouerdane W, et al (2018) Ranking with multiple reference points: efficient elicitation and learning procedures. p 8, In: Proceeding from multiple criteria Decision aid to preference learning (DA2PL)
Benabbou N, Perny P (2016) Solving multi-agent knapsack problems using incremental approval voting. In: Proceedings of the twenty-second european conference on artificial intelligence, p 1318-1326
Benabbou N, Perny P (2018) Interactive resolution of multiobjective combinatorial optimization problems by incremental elicitation of criteria weights. EURO J Decision Processes 6(3):283–319
Benabbou N, Gonzales C, Perny P et al (2015) Minimax regret approaches for preference elicitation with rank-dependent aggregators. EURO J Decision Processes 3(1):29–64
Benabbou N, Perny P, Viappiani P (2016) Incremental preference elicitation in multi-attribute domains for choice and ranking with the Borda count. In: Proceedings of SUM’16, pp 81–95
Benabbou N, Perny P, Viappiani P (2017) Incremental elicitation of Choquet capacities for multicriteria choice, ranking and sorting problems. Artif Intell 246:152–180
Boutilier C, Patrascu R, Poupart P et al (2006) Constraint-based optimization and utility elicitation using the minimax decision criterion. Artif Intell 170(8):686–713. https://doi.org/10.1016/j.artint.2006.02.003
Bouyssou D, Marchant T (2013) Multiattribute preference models with reference points. Eur J Oper Res 229(2):470–481. https://doi.org/10.1016/j.ejor.2013.02.046
Bouyssou D, Pirlot M (2009) An axiomatic analysis of concordance-discordance relations. Eur J Oper Res 199(2):468–477. https://doi.org/10.1016/j.ejor.2008.11.011
Bouyssou D, Marchant T, Pirlot M, et al (2006) Evaluation and decision models with multiple criteria: stepping stones for the analyst, 1st edn. In: International series in operations research and management science, Volume 86, Boston
Butler J, Jia J, Dyer J (1997) Simulation techniques for the sensitivity analysis of multi-criteria decision models. Eur J Oper Res 103(3):531–546
Chajewska U, Koller D, Parr R (2000) Making rational decisions using adaptive utility elicitation. AAAI Press / The MIT Press
Figueira J, Greco S, Ehrgott M (2005) Multiple criteria decision analysis: state of the art surveys. Springer, Boston, Dordrecht, London
Figueira J, Mousseau V, Roy B (2005) ELECTRE methods. In: Figueira J, Greco S, Ehrgott M (eds) Multiple criteria decision analysis: state of the art surveys. Springer, Boston, Dordrecht, London, pp 133–162
Fürnkranz J, Hüllermeier E (2011) Preference learning and ranking by pairwise comparison. Springer, Berlin and Heidelberg, pp 65–82
Gilbert H, Benabbou N, Perny P, et al (2017) Incremental decision making under risk with the weighted expected utility model. In: Proceedings of the twenty-sixth international joint conference on artificial intelligence (IJCAI), pp 4588–4594
Greco S, Matarazzo B, Slowinski R (2001) Rough sets theory for multicriteria decision analysis. Eur J Operat Res 129(1):1–47. https://doi.org/10.1016/S0377-2217(00)00167-3
Jacquet-Lagrèze E, Siskos Y (2001) Preference disaggregation: 20 years of mcda experience. Eur J Operat Res 130(2):233–245
Keeney R, Raiffa H (1976) Decisions with multiple consequences: preferences and value tradeoffs. Wiley, New York, NY
Kendall MG (1938) A new measure of rank correlation. Biometrika 30(1/2):81–93
Khannoussi A, Olteanu AL, Dezan C, et al (2018) Incremental learning of simple ranking method using reference profiles models. In: DA2PL2018: from multiple criteria decision aid to preference learning, Poznan, Poland
Khannoussi A, Olteanu AL, Labreuche C et al (2021) Simple ranking method using reference profiles incremental elicitation of the preference parameters. 4OR: A Quart J Operat Res. https://doi.org/10.1007/s10288-021-00487-w
Liu J, Ouerdane W, Mousseau V (2014) Metaheuristic approach for preference learning in multicriteria ranking based on reference points, In: Proceeding of the 2nd wokshop from multiple criteria Decision aid to Preference Learning (DA2PL)
Lu T, Boutilier C (2011) Robust approximation and incremental elicitation in voting protocols. In: Proceedings of IJCAI’11, pp 287–293
Mousseau V (1995) Eliciting information concerning the relative importance of criteria. In: Pardalos P, Siskos Y, Zopounidis C (eds) Advances in multicriteria analysis. Kluwer Academic, Dordrecht, pp 17–43
Olteanu AL, Belahcène K, Mousseau V et al (2022) Preference elicitation for a ranking method based on multiple reference profiles. 4OR-Q J Oper Res 20:63–84. https://doi.org/10.1007/s10288-020-00468-5
Regan K, Boutilier C (2009) Regret-based reward elicitation for Markov decision processes. In: UAI, pp 444–451
Rolland A (2013) Reference-based preferences aggregation procedures in multi-criteria decision making. Eur J Oper Res 225(3):479–486. https://doi.org/10.1016/j.ejor.2012.10.013
Roy B (1991) The outranking approach and the foundations of electre methods. Theor Decis 31(1):49–73
Roy B (1996) Multicriteria methodology for decision aiding. Kluwer Academic, Dordrecht
White CC, Sage AP, Dozono S (1984) A model of multiattribute decisionmaking and trade-off weight determination under uncertainty. IEEE Trans Syst Man Cybern 14(2):223–229
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Khannoussi, A., Olteanu, AL., Meyer, P. et al. A regret-based query selection strategy for the incremental elicitation of the criteria weights in an SRMP model. Oper Res Int J 24, 12 (2024). https://doi.org/10.1007/s12351-024-00823-y
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DOI: https://doi.org/10.1007/s12351-024-00823-y
Keywords
- Multi-criteria decision aiding
- Incremental preference elicitation
- Query selection strategy
- Regret-based approach