Abstract
The paper compares two graphical approaches proposed for the qualitative modeling of preferences: \(\pi \)-pref nets and LP-trees. The former uses the graphical setting of possibilistic networks for completing partial specifications of user preferences, while the latter, which is based on lexicographic ordering, appears to offer a convenient framework for learning preferences. The \(\pi \)-pref network representation appears to be more flexible, even if the addition of very specific constraints allows us to recover the total order of the LP-trees.
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
References
Ben Amor, N., Benferhat, S.: Graphoid properties of qualitative possibilistic independence relations. Int. J. Uncert. Fuzz. Know.-Based Syst. 13, 59–96 (2005)
Ben Amor, N., Dubois, D., Gouider, H., Prade, H.: Preference modeling with possibilistic networks and symbolic weights: a theoretical study. In: Proceedings of the 22nd European Conference on Artificial Intelligence (ECAI 2016), The Hague, 29 August–2 September, pp. 1203–1211 (2016)
Ben Amor, N., Dubois, D., Gouider, H., Prade, H.: Graphical models for preference representation: an overview. In: Schockaert, S., Senellart, P. (eds.) SUM 2016. LNCS (LNAI), vol. 9858, pp. 96–111. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45856-4_7
Ben Amor, N., Dubois, D., Gouider, H., Prade, H.: Possibilistic preference networks. Inf. Sci. 460–461, 401–415 (2018)
Benferhat, S., Dubois, D., Garcia, L., Prade, H.: On the transformation between possibilistic logic bases and possibilistic causal networks. Int. J. Approx. Reas. 29, 135–173 (2002)
Booth, R., Chevaleyre, Y., Lang, J., Mengin, J., Sombattheera, C.: Learning conditionally lexicographic preference relations. In: Proceedings of the 19th European Conference on Artificial Intelligence (ECAI), pp. 269–274 (2010)
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)
Brafman, R.I., Domshlak, C., Shimony, S.E.: On graphical modeling of preference and importance. J. Artif. Intell. Res. 25, 389–424 (2006)
Bräuning, M., Hüllermeier, E.: Learning conditional lexicographic preference trees. In: Fürnkranz, J., Hüllermier (eds.) Proceedings of ECAI 2012 Workshop on Preference Learning of the European Conference on Artiticial Intelligence, Montpellier (2012)
Dubois, D., Hadjali, A., Prade, H., Touazi, F.: Erratum to: database preference queries - a possibilistic logic approach with symbolic priorities. Ann. Math. Artif. Intell. 73(3–4), 359–363 (2015)
Dubois, D., Prade, H.: Qualitative and semi-quanlitative modeling of uncertain knowledge - a discussion. In: Computational Models of Rationality. Essays Dedicated to Gabriele Kern-Isberner on the Occasion of Her 60th Birthday, pp. 280–292. College Publications (2016)
Eichhorn, Ch., Fey, M., Kern-Isberner, G.: CP- and OCF-networks - a comparison. Fuzzy Sets Syst. 298, 109–127 (2016)
Fargier, H., Gimenez, P.-F., Mengin, J.: Learning lexicographic preference trees from positive examples. In: Proceedings of the 32nd Conference on Artificial Intelligence (AAAI 2018), pp. 2959–2966 (2018)
Fürnkranz, J., Hüllermeier, E. (eds.): Preference Learning. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-14125-6
Gigerenzer, G., Goldstein, D.G.: Reasoning the fast and frugal way: models of bounded rationality. Psychol. Rev. 103(4), 650–669 (1996)
Huelsman, M., Truszczynski, M.: A lexicographic strategy for approximating dominance in CP-nets. In: Proceedings of the 33rd International Florida Artificial Intelligence Research Society Conference (FLAIRS), pp. 59–74 (2020)
Kaci, S., Lang, J., Perny, P.: Compact representation of preferences. In: Marquis, P., Papini, O., Prade, H. (eds.) A Guided Tour of Artificial Intelligence Research, pp. 217–252. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-06164-7_7
Liu, X., Truszczynski, M.: Learning partial lexicographic preference trees over combinatorial domains. In: Bonet, B., Koenig, S. (eds.) Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI 2015), Austin, Texas, 25–30 January, pp. 1539–1545. AAAI Press (2015)
Lang, J., Mengin, J., **a, L.: Aggregating conditionally lexicographic preferences on multi-issue domains. In: Proceedings of the 18th International Conference on Principles and Practice of Constraint Program (CP 2012), pp. 973–987 (2012)
Wilson, N.: Computational techniques for a simple theory of conditional preferences. Artif. Intell. 175, 1053–1091 (2011)
Wilson, N., Dubois, D., Prade, H.: CP-nets, \(\pi \)-pref nets, and pareto dominance. In: Ben Amor, N., Quost, B., Theobald, M. (eds.) SUM 2019. LNCS (LNAI), vol. 11940, pp. 169–183. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35514-2_13
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Ben Amor, N., Dubois, D., Prade, H., Saidi, S. (2022). Possibilistic Preference Networks and Lexicographic Preference Trees – A Comparison. In: Ciucci, D., et al. Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2022. Communications in Computer and Information Science, vol 1601. Springer, Cham. https://doi.org/10.1007/978-3-031-08971-8_48
Download citation
DOI: https://doi.org/10.1007/978-3-031-08971-8_48
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-08970-1
Online ISBN: 978-3-031-08971-8
eBook Packages: Computer ScienceComputer Science (R0)