Decomposition Strategies for Constructive Preference Elicitation

TL;DR

This paper introduces a decomposition approach for constructive preference elicitation that reduces user cognitive load and computational complexity by focusing on partial configurations rather than full solutions.

Contribution

The authors propose a novel decomposition technique for large preference-based decision problems that leverages partial configurations to improve efficiency and user experience.

Findings

Significant reduction in user cognitive load.

Part-wise inference can be exponentially faster.

Promising empirical results on synthetic and real problems.

Abstract

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 itera tively by interacting with the user. Previous work has shown that Coactive Learning is a suitable method for learning user preferences in constructive scenarios. In Coactive Learning the user provides feedback to the algorithm in the form of an improvement to a suggested configuration. When the problem involves many decision variables and constraints, this type of interaction poses a significant cognitive burden on the user. We propose a decomposition technique for large preference-based decision problems relying exclusively on inference and feedback over partial configurations. This has the clear advantage of drastically reducing the user cognitive load. Additionally, part-wise inference can be (up to exponentially) less computationally demanding than inference over full configurations. We discuss the theoretical implications of working with parts and present promising empirical results on one synthetic and two realistic constructive problems.