mCP nets: representing and reasoning with preferences of multiple agents
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Journal of Artificial Intelligence Research
Voting on multiattribute domains with cyclic preferential dependencies
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Agreeing on social outcomes using individual CP-nets
Multiagent and Grid Systems - Planning in multiagent systems
Generalized solution techniques for preference-based constrained optimization with CP-nets
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
An Efficient Procedure for Collective Decision-making with CP-nets
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Influence and aggregation of preferences over combinatorial domains
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
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CP-nets are an attractive model for representing individual preferences, in part because they allow us to find the best outcome for an agent in time that is proportional to just the number of features in an outcome. In this paper, we investigate whether similar efficiencies can apply to finding the best social outcome for agents whose individual preferences are captured in CP-nets. Because CP-nets provide only qualitative information, we adopt a way to compare outcomes across agents based on each outcome's relative standing in the individuals' spaces of possible outcomes. This in turn guides the search through the outcome preference graphs that are induced by the agents' CP-nets to find the optimal social outcome. Because these induced preference graphs are exponential in the number of features, we examine the conditions under which the agents can search directly using their CP-nets, and show that our approach yields near-optimal social outcomes in exponentially less time.