Constructive Preference Elicitation over Hybrid Combinatorial Spaces

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...

Learning Modulo Theories for preference elicitation in hybrid domains

This paper introduces CLEO, a novel preference elicitation algorithm capable of recommending complex objects in hybrid domains, characterized by both discrete and continuous attributes and constraints defined over them. The algorithm assumes minimal initial information, i.e., a set of catalog attributes, and defines decisional features as logic formulae combining Boolean and algebraic constrain...

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...

Constructive Preference Elicitation for Multiple Users with Setwise Max-margin

Constructing and Clearing Combinatorial Exchanges Using Preference Elicitation

Combinatorial exchanges arise naturally in multi-agent systems that execute hierarchically decomposed tasks, when the agents have uncertainty about each other’s tasks and each other’s capability of handling tasks. In a combinatorial exchange, the information is aggregated to one party who then decides the task allocation among the agents. Unfortunately, such exchanges can require that bidders c...