Decomposition Strategies for Constructive Preference Elicitation

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...

Constructive Preference Elicitation for Multiple Users with Setwise Max-margin

Constructive Preference Elicitation

When faced with large or complex decision problems, human decision makers (DM) can make costly mistakes, due to inherent limitations of their memory, attention, and knowledge. Preference elicitation tools assist the decision maker in overcoming these limitations. They do so by interactively learning the DM’s preferences through appropriately chosen queries and suggesting high-quality outcomes b...

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 ...

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...