Constructive Preference Elicitation

Constructive Preference Elicitation for Multiple Users with Setwise Max-margin

Constructive Preference Elicitation over Hybrid Combinatorial Spaces

Peference elicitation is the task of suggesting a highly preferred configuration to a decision maker. The preferences are typically learned by querying the user for choice feedback over pairs or sets of objects. In its constructive variant, new objects are synthesized “from scratch” by maximizing an estimate of the user utility over a combinatorial (possibly infinite) space of candidates. In th...

Constructed Preferences and Value-focused Thinking: Implications for AI research on Preference Elicitation

Decision theory has changed considerably in the last decade. In behavioral decision theory, a large number of studies have shown that human decision making is inherently adaptive and constructive. In prescriptive decision theory, we have witnessed a move from an alternative-focused approach to a value-focused approach. In this paper, we discuss the implications of these new ideas in behavioral ...

Decomposition Strategies for Constructive Preference Elicitation

We tackle the problem of constructive preference elicitation, that is the problem of learning user preferences over very large decision problems, involving a combinatorial space of possible outcomes. In this setting, the suggested configuration is synthesized on-the-fly by solving a constrained optimization problem, while the preferences are learned iteratively by interacting with the user. Pre...

Constructive Preference Elicitation by Setwise Max-Margin Learning

In this paper we propose an approach to preference elicitation that is suitable to large configuration spaces beyond the reach of existing state-of-theart approaches. Our setwise max-margin method can be viewed as a generalization of max-margin learning to sets, and can produce a set of “diverse” items that can be used to ask informative queries to the user. Moreover, the approach can encourage...