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We tackle the problem of constructive preference elicitation, that is theproblem of learning user preferences over very large decision problems,involving a combinatorial space of possible outcomes. In this setting, thesuggested configuration is synthesized on-the-fly by solving a constrainedoptimization problem, while the preferences are learned itera tively byinteracting with the user. Previous work has shown that Coactive Learning is asuitable method for learning user preferences in constructive scenarios. InCoactive Learning the user provides feedback to the algorithm in the form of animprovement to a suggested configuration. When the problem involves manydecision variables and constraints, this type of interaction poses asignificant cognitive burden on the user. We propose a decomposition techniquefor large preference-based decision problems relying exclusively on inferenceand feedback over partial configurations. This has the clear advantage ofdrastically reducing the user cognitive load. Additionally, part-wise inferencecan be (up to exponentially) less computationally demanding than inference overfull configurations. We discuss the theoretical implications of working withparts and present promising empirical results on one synthetic and tworealistic constructive problems.

Decomposition Strategies for Constructive Preference Elicitation