Abstract
The development of models that can cope with noisy input preferences is a critical topic in artificial intelligence methods for interactive preference elicitation. A Bayesian representation of the uncertainty in the user preference model can be used to successfully handle this, but there are large costs in terms of the processing time required to update the probabilistic model upon receiving the user’s answers, to compute the optimal recommendation and to select the next queries to ask; these costs limit the adoption of these techniques in real-time contexts. A Bayesian approach also requires one to assume a prior distribution over the set of user preference models. In this work, dealing with multi-criteria decision problems, we consider instead a more qualitative approach to preference uncertainty, focusing on the most plausible user preference models, and aim to generate a query strategy that enables us to find an alternative that is optimal in all of the most plausible preference models. We develop a non-Bayesian algorithmic method for recommendation and interactive elicitation that considers a large number of possible user models that are evaluated with respect to their degree of consistency of the input preferences. This suggests methods for generating queries that are reasonably fast to compute. Our test results demonstrate the viability of our approach, including in real-time contexts, with high accuracy in recommending the most preferred alternative for the user.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
Only the possibly optimal alternatives in a set \(A\) of alternatives are relevant, so if we didn’t enforce that all alternatives are possibly optimal, then we would effectively be dealing with a (perhaps very much) smaller problem.
References
Adam, L., Destercke, S.: Possibilistic preference elicitation by minimax regret. In: de Campos, C.P., Maathuis, M.H., Quaeghebeur, E. (eds.) Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, UAI 2021, Virtual Event, 27–30 July 2021. Proceedings of Machine Learning Research, vol. 161, pp. 718–727. AUAI Press (2021)
Blythe, J.: Visual exploration and incremental utility elicitation. In: Eighteenth National Conference on Artificial Intelligence, pp. 526–532. American Association for Artificial Intelligence, USA (2002)
Bourdache, N., Perny, P.: Anytime algorithms for adaptive robust optimization with OWA and WOWA. In: Rothe, J. (ed.) ADT 2017. LNCS (LNAI), vol. 10576, pp. 93–107. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67504-6_7
Bourdache, N., Perny, P., Spanjaard, O.: Incremental elicitation of rank-dependent aggregation functions based on Bayesian linear regression. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19, pp. 2023–2029 (2019). https://doi.org/10.24963/ijcai.2019/280
Bourdache, N., Perny, P., Spanjaard, O.: Bayesian preference elicitation for multiobjective combinatorial optimization. In: DA2PL 2020 - From Multiple Criteria Decision Aid to Preference Learning, Trento, Italy (2020). https://hal.archives-ouvertes.fr/hal-02979845
Bourdache, N., Perny, P., Spanjaard, O.: Bayesian preference elicitation for multiobjective combinatorial optimization. CoRR abs/2007.14778 (2020). https://arxiv.org/abs/2007.14778
Boutilier, C.: A POMDP formulation of preference elicitation problems. In: Proceedings of AAAI02, pp. 239–246 (2002)
Boutilier, C.: Computational decision support regret-based models for optimization and preference elicitation. In: Comparative Decision Making. Oxford University Press (2013). https://doi.org/10.1093/acprof:oso/9780199856800.003.0041
Chajewska, U., Getoor, L., Norman, J., Shahar, Y.: Utility elicitation as a classification problem. In: UAI 1998: Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 79–88. Morgan Kaufmann (1998)
Chajewska, U., Koller, D., Parr, R.: Making rational decisions using adaptive utility elicitation. In: Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on on Innovative Applications of Artificial Intelligence, pp. 363–369 (2000)
Dyer, J.S.: Interactive goal programming. Manage. Sci. 19(1), 62–70 (1972). https://doi.org/10.1287/mnsc.19.1.62
Keeney, R.L., Raiffa, H., Meyer, R.: Decisions with Multiple Objectives: Preferences and Value Trade-Offs. Wiley series in probability and mathematical statistics. Applied probability and statistics. Cambridge University Press (1993)
Pourkhajouei, S., Toffano, F., Viappiani, P., Wilson, N.: An efficient non-Bayesian approach for interactive preference elicitation under noisy preference models (longer version) (2023). http://ucc.insight-centre.org/nwilson/ECSQARU23longer.pdf
Price, R., Messinger, P.R.: Optimal recommendation sets: covering uncertainty over user preferences. In: AAAI (2005)
Salo, A., Hämäläinen, R.P.: Preference ratios in multiattribute evaluation (prime)-elicitation and decision procedures under incomplete information. IEEE Trans. Syst. Man Cybern. Part A 31, 533–545 (2001)
Sauré, D., Vielma, J.P.: Ellipsoidal methods for adaptive choice-based conjoint analysis. Oper. Res. 67(2), 315–338 (2019). https://doi.org/10.1287/opre.2018.1790
Steuer, R.E., Choo, E.U.: An interactive weighted Tchebycheff procedure for multiple objective programming. Math. Program. 26(3), 326–344 (1983)
Teso, S., Passerini, A., Viappiani, P.: Constructive preference elicitation by setwise max-margin learning. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, pp. 2067–2073 (2016)
Toffano, F., Viappiani, P., Wilson, N.: Efficient exact computation of setwise minimax regret for interactive preference elicitation. In: AAMAS 2021: 20th International Conference on Autonomous Agents and Multiagent Systems, pp. 1326–1334 (2021). https://doi.org/10.5555/3463952.3464105
Toffano, F., Wilson, N.: Minimality and comparison of sets of multi-attribute vectors. Auton. Agent. Multi-Agent Syst. 36(2), 1–66 (2022)
Vendrov, I., Lu, T., Huang, Q., Boutilier, C.: Gradient-based optimization for Bayesian preference elicitation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 06, pp. 10292–10301 (2020). https://doi.org/10.1609/aaai.v34i06.6592
Viappiani, P., Boutilier, C.: On the equivalence of optimal recommendation sets and myopically optimal query sets. Artif. Intell. 286, 103328 (2020). https://doi.org/10.1016/j.artint.2020.103328, https://www.sciencedirect.com/science/article/pii/S0004370220300849
White III, C.C., Sage, A.P., Dozono, S.: A model of multiattribute decisionmaking and trade-off weight determination under uncertainty. IEEE Trans. Syst. Man Cybern. 14(2), 223–229 (1984). https://doi.org/10.1109/TSMC.1984.6313205
Zionts, S., Wallenius, J.: An interactive programming method for solving the multiple criteria problem. Manage. Sci. 22(6), 652–663 (1976)
Acknowledgment
This publication has emanated from research conducted with the financial support of Science Foundation Ireland under Grant number 12/RC/2289-P2, which is co-funded under the European Regional Development Fund; and with the support of the EU Network of Excellence project, TAILOR.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Pourkhajouei, S., Toffano, F., Viappiani, P., Wilson, N. (2024). An Efficient Non-Bayesian Approach for Interactive Preference Elicitation Under Noisy Preference Models. In: Bouraoui, Z., Vesic, S. (eds) Symbolic and Quantitative Approaches to Reasoning with Uncertainty. ECSQARU 2023. Lecture Notes in Computer Science(), vol 14294. Springer, Cham. https://doi.org/10.1007/978-3-031-45608-4_23
Download citation
DOI: https://doi.org/10.1007/978-3-031-45608-4_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-45607-7
Online ISBN: 978-3-031-45608-4
eBook Packages: Computer ScienceComputer Science (R0)