Two Proof Procedures for a Cardinality Based Language in Propositional Calculus
STACS '94 Proceedings of the 11th Annual Symposium on Theoretical Aspects of Computer Science
Hard and soft constraints for reasoning about qualitative conditional preferences
Journal of Heuristics
mCP nets: representing and reasoning with preferences of multiple agents
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Strongly decomposable voting rules on multiattribute domains
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Voting on multiattribute domains with cyclic preferential dependencies
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
The computational complexity of dominance and consistency in CP-Nets
Journal of Artificial Intelligence Research
Hypercubewise preference aggregation in multi-issue domains
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Qualitative preference-based service selection for multiple agents
Web Intelligence and Agent Systems
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This paper studies the problem of majority-rule-based collective decision-making where the agents' preferences are represented by CP-nets (Conditional Preference Networks). As there are exponentially many alternatives, it is impractical to reason about the individual full rankings over the alternative space and apply majority rule directly. Most existing works either do not consider computational requirements, or depend on a strong assumption that the agents have acyclic CP-nets that are compatible with a common order on the variables. To this end, this paper proposes an efficient SAT-based approach, called MajCP (Majority-rule-based collective decision-making with CP-nets), to compute the majority winning alternatives. Our proposed approach only requires that each agent submit a CP-net; the CP-net can be cyclic, and it does not need to be any common structures among the agents' CP-nets. The experimental results presented in this paper demonstrate that the proposed approach is computationally efficient. It offers several orders of magnitude improvement in performance over a Brute-force algorithm for large numbers of variables.