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. 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 preferencebased 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.

Decomposition Strategies for Constructive Preference Elicitation / Dragone, P.; Teso, S.; Kumar, M.; and Passerini, A.. - (2018), pp. 2934-2942. ((Intervento presentato al convegno AAAI 2018 tenutosi a New Orleans, LA, USA nel February 2-7, 2018.

Decomposition Strategies for Constructive Preference Elicitation

Dragone, P.;Teso, S.;and Passerini, A.
2018-01-01

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 iteratively 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 preferencebased 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.
Proceedings of the 32nd Conference on Artificial Intelligence (AAAI).
Menlo Park, California, USA
AAAI Press
Dragone, P.; Teso, S.; Kumar, M.; and Passerini, A.
Decomposition Strategies for Constructive Preference Elicitation / Dragone, P.; Teso, S.; Kumar, M.; and Passerini, A.. - (2018), pp. 2934-2942. ((Intervento presentato al convegno AAAI 2018 tenutosi a New Orleans, LA, USA nel February 2-7, 2018.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/200482
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