Complexity of manipulating elections with few candidates
Eighteenth national conference on Artificial intelligence
Vote elicitation: complexity and strategy-proofness
Eighteenth national conference on Artificial intelligence
Logical Preference Representation and Combinatorial Vote
Annals of Mathematics and Artificial Intelligence
mCP nets: representing and reasoning with preferences of multiple agents
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Complexity of terminating preference elicitation
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Uncertainty in preference elicitation and aggregation
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Determining possible and necessary winners under common voting rules given partial orders
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
A multivariate complexity analysis of determining possible winners given incomplete votes
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Compiling the votes of a subelectorate
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Preference aggregation over restricted ballot languages: sincerity and strategy-proofness
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Where are the really hard manipulation problems? the phase transition in manipulating the veto rule
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Agreeing on social outcomes using individual CP-nets
Multiagent and Grid Systems - Planning in multiagent systems
Towards a dichotomy for the Possible Winner problem in elections based on scoring rules
Journal of Computer and System Sciences
An Efficient Procedure for Collective Decision-making with CP-nets
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
On problem kernels for possible winner determination under the k-approval protocol
MFCS'10 Proceedings of the 35th international conference on Mathematical foundations of computer science
Comparing multiagent systems research in combinatorial auctions and voting
Annals of Mathematics and Artificial Intelligence
Aggregating value ranges: preference elicitation and truthfulness
Autonomous Agents and Multi-Agent Systems
Practical voting rules with partial information
Autonomous Agents and Multi-Agent Systems
On the modelling and optimization of preferences in constraint-based temporal reasoning
Artificial Intelligence
Preferences in AI: An overview
Artificial Intelligence
Incompleteness and incomparability in preference aggregation: Complexity results
Artificial Intelligence
Possible and necessary winners in voting trees: majority graphs vs. profiles
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Possible winners when new alternatives join: new results coming up!
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
Determining possible and necessary winners under common voting rules given partial orders
Journal of Artificial Intelligence Research
Is computational complexity a barrier to manipulation?
Annals of Mathematics and Artificial Intelligence
Winner determination in voting trees with incomplete preferences and weighted votes
Autonomous Agents and Multi-Agent Systems
Voting with partial information: what questions to ask?
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Ordering based decision making - A survey
Information Fusion
Bribery in voting with CP-nets
Annals of Mathematics and Artificial Intelligence
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We consider how to combine the preferences of multiple agents despite the presence of incompleteness and incomparability in their preference orderings. An agent's preference ordering may be incomplete because, for example, there is an ongoing preference elicitation process. It may also contain incomparability as this is useful, for example, in multi-criteria scenarios. We focus on the problem of computing the possible and necessary winners, that is, those outcomes which can be or always are the most preferred for the agents. Possible and necessary winners are useful in many scenarios including preference elicitation. First we show that computing the sets of possible and necessary winners is in general a difficult problem as is providing a good approximation of such sets. Then we identify general properties of the preference aggregation function which are sufficient for such sets to be computed in polynomial time. Finally, we show how possible and necessary winners can be used to focus preference elicitation.