Constraint-based preferences via utility hyper-Graphs

Rafik Hadfi, Takayuki Lto

Research output: Chapter in Book/Report/Conference proceedingConference PaperOtherpeer-review


Real-world decisions involve preferences that are nonlinear and often defined over multiple and interdependent issues. Such scenarios are known to be challenging, especially in strategic encounters between agents having distinct constraints and preferences. In this case, reaching an agreement becomes more difficult as the search space and the complexity of the problem grow. In this paper, we propose a new representation for constraint- based utility spaces that can tackle the scalability problem by efficiently finding the optimal contracts. Particularly, the constraint-based utility space is mapped into an issue- constraint hyper-graph. Exploring the utility space reduces then to a message passing mechanism along the hyper-edges by means of utility propagation. We experimentally evaluate the model using parameterized random nonlinear utility spaces. We show that it can handle a large family of complex utility spaces by finding the optimal contract(s), outperforming previous sampling-based approaches.

Original languageEnglish
Title of host publicationMultidisciplinary Workshop on Advances in Preference Handling
Subtitle of host publicationPapers Presented at the 28th AAAI Conference on Artificial Intelligence, Technical Report
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Number of pages6
ISBN (Electronic)9781577356714
Publication statusPublished - 1 Jan 2014
Externally publishedYes
EventAAAI Conference on Artificial Intelligence Workshops 2014: Advances in Preference Handling - Quebec City, Canada
Duration: 28 Jul 201428 Jul 2014
Conference number: 28th


ConferenceAAAI Conference on Artificial Intelligence Workshops 2014
CityQuebec City

Cite this