Abstract
In this paper, we consider Preference Inference based on a generalised form of Pareto order. Preference Inference aims at reasoning over an incomplete specification of user preferences. We focus on two problems. The Preference Deduction Problem (PDP) asks if another preference statement can be deduced (with certainty) from a set of given preference statements. The Preference Consistency Problem (PCP) asks if a set of given preference statements is consistent, i.e., the statements are not contradicting each other. Here, preference statements are direct comparisons between alternatives (strict and non-strict). It is assumed that a set of evaluation functions is known by which all alternatives can be rated. We consider Pareto models which induce order relations on the set of alternatives in a Pareto manner, i.e., one alternative is preferred to another only if it is preferred on every component of the model.
We describe characterisations for deduction and consistency based on an analysis of the set of evaluation functions, and present algorithmic solutions and complexity results for PDP and PCP, based on Pareto models in general and for a special case. Furthermore, a comparison shows that the inference based on Pareto models is less cautious than some other types of well-known preference model.
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
References
Abraham, D.J., Cechlárová, K., Manlove, D.F., Mehlhorn, K.: Pareto optimality in house allocation problems. In: Deng, X., Du, D.-Z. (eds.) ISAAC 2005. LNCS, vol. 3827, pp. 1163–1175. Springer, Heidelberg (2005)
Borzsonyi, S., Kossmann, D., Stocker, K.: The skyline operator. In: 17th International Conference On Data Engineering, pp. 421–430. IEEE Computer Society, Heidelberg (2001)
Bridge, D., Ricci, F.: Supporting product selection with query editing recommendations. In: 1st ACM Conference on Recommender Systems (RecSys 2007), pp. 65–72. ACM Press, Minneapolis (2007)
George, A.-M., Razak, A., Wilson, N.: The comparison of multi-objective preference inference based on lexicographic and weighted average models. In: 27th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2015), pp. 88–95. IEEE Press, Vietri sul Mare (2015)
George, A.-M., Wilson, N., O’Sullivan, B.: Towards fast algorithms for the preference consistency problem based on hierarchical models. In: 25th IEEE International Joint Conference on Artificial Intelligence (IJCAI 2016). AAAI Press, New York (2016, to be published)
Figueira, J., Greco, S., Ehrgott, M.: Multiple Criteria Decision Analysis: State of the Art Surveys. Springer, London (2005)
Maarry, K.E., Lofi, C., Balke, W.-T.: Crowdsourcing for query processing on web data: a case study on the skyline operator. J. Comput. Inf. Technol. (CIT) 23(1), 43–60 (2015)
Marinescu, R., Razak, A., Wilson, N.: Multi-objective constraint optimization with tradeoffs. In: Schulte, C. (ed.) CP 2013. LNCS, vol. 8124, pp. 497–512. Springer, Heidelberg (2013)
Trabelsi, W., Wilson, N., Bridge, D., Ricci, F.: Preference dominance reasoning for conversational recommender systems: a comparison between a comparative preferences and a sum of weights approach. Int. J. Artif. Intell. Tools 20, 591–616 (2011)
Wallis, W.D.: The Mathematics of Elections and Voting. Springer, Heidelberg (2014)
Wilson, N.: Efficient inference for expressive comparative preference languages. In: Twenty-First International Joint Conference on Artificial Intelligence (IJCAI 2009), pp. 961–966. AAAI Press, Pesadena (2009)
Wilson, N.: Preference inference based on lexicographic models. In: 21st European Conference on Artificial Intelligence (ECAI 2014), pp. 921–926. IOS Press, Prague (2014)
Wilson, N., George, A.-M., O’Sullivan, B., B.: Computation and complexity of preference inference based on hierarchical models. In: 24th IEEE International Joint Conference on Artificial Intelligence (IJCAI 2015), pp. 3271–3277. AAAI Press, Buenos Aires (2015)
Acknowledgments
This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
George, AM., Wilson, N. (2016). Preference Inference Based on Pareto Models. In: Schockaert, S., Senellart, P. (eds) Scalable Uncertainty Management. SUM 2016. Lecture Notes in Computer Science(), vol 9858. Springer, Cham. https://doi.org/10.1007/978-3-319-45856-4_12
Download citation
DOI: https://doi.org/10.1007/978-3-319-45856-4_12
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45855-7
Online ISBN: 978-3-319-45856-4
eBook Packages: Computer ScienceComputer Science (R0)