In this paper, we address the issues of gathering the preferences of a user when they may
be uncertain, and of handling the possible ensuing inconsistency. We suggest using …

Decisions under uncertainty or with multiple objectives usually require the decision maker to
formulate a preference regarding risks or trade-offs. If this preference is known, the ordered …

This paper introduces an interactive framework to guide decision-makers in a multi-criteria
supplier selection process. State-of-the-art multi-criteria methods for supplier selection elicit …

Bundle recommendation aims to generate bundles of associated products that users tend to
consume as a whole under certain circumstances. Modeling the bundle utility for users is a …

One way to make decisions under uncertainty is to select an optimal option from a possible
range of options, by maximizing the expected utilities derived from a probability model …

In a decision-making problem, there is often some uncertainty regarding the user
preferences. We assume a parameterised utility model, where in each scenario we have a …

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 …

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 …

Many real-world problems have multiple, conflicting objectives. Without knowing the utility
function of the decision maker, one must extensively learn all Pareto-efficient trade-offs to …

A key issue in artificial intelligence methods for interactive preference elicitation is choosing
at each stage an appropriate query to the user, in order to find a near-optimal solution as …