This paper studies the problem of collective decision-making in the case where the agents' preferences are represented by CP-nets (conditional preference networks). In many real-world decision-making problems, the number of possible outcomes is exponential in the number of domain variables. Most related works either do not consider computational concerns, or depend on a strong assumption that all the agents' CP-nets share a common preferential-independence structure. To this end, we introduce a novel procedure for collective decision-making with CP-nets. Our proposed approach allows the agents to have different preferential-independence structures and guarantees Pareto-optimality. Our experimental results demonstrate that our proposed procedure is computationally efficient and produces the results that are close to the fair Minimax solution.
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