Preference elicitation in combinatorial auctions
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Making Rational Decisions Using Adaptive Utility Elicitation
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A POMDP formulation of preference elicitation problems
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On polynomial-time preference elicitation with value queries
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Applying learning algorithms to preference elicitation
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Preference Elicitation and Query Learning
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Combinatorial Auctions
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Eliciting single-peaked preferences using comparison queries
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Uncertainty in preference elicitation and aggregation
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Toward case-based preference elicitation: similarity measures on preference structures
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Aggregating value ranges: preference elicitation and truthfulness
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The complexity of manipulative attacks in nearly single-peaked electorates
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Where are the hard manipulation problems?
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Clone structures in voters' preferences
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Voting with partial information: what questions to ask?
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Kemeny elections with bounded single-peaked or single-crossing width
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Efficient vote elicitation under candidate uncertainty
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Multi-dimensional single-peaked consistency and its approximations
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
The complexity of manipulative attacks in nearly single-peaked electorates
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On the computation of fully proportional representation
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Voting is a general method for aggregating the preferences of multiple agents. Each agent ranks all the possible alternatives, and based on this, an aggregate ranking of the alternatives (or at least a winning alternative) is produced. However, when there are many alternatives, it is impractical to simply ask agents to report their complete preferences. Rather, the agents' preferences, or at least the relevant parts thereof, need to be elicited. This is done by asking the agents a (hopefully small) number of simple queries about their preferences, such as comparison queries, which ask an agent to compare two of the alternatives. Prior work on preference elicitation in voting has focused on the case of unrestricted preferences. It has been shown that in this setting, it is sometimes necessary to ask each agent (almost) as many queries as would be required to determine an arbitrary ranking of the alternatives. In contrast, in this paper, we focus on single-peaked preferences. We show that such preferences can be elicited using only a linear number of comparison queries, if either the order with respect to which preferences are single-peaked is known, or at least one other agent's complete preferences are known. We show that using a sublinear number of queries does not suffice. We also consider the case of cardinally single-peaked preferences. For this case, we show that if the alternatives' cardinal positions are known, then an agent's preferences can be elicited using only a logarithmic number of queries; however, we also show that if the cardinal positions are not known, then a sublinear number of queries does not suffice. We present experimental results for all elicitation algorithms. We also consider the problem of only eliciting enough information to determine the aggregate ranking, and show that even for this more modest objective, a sublinear number of queries per agent does not suffice for known ordinal or unknown cardinal positions. Finally, we discuss whether and how these techniques can be applied when preferences are almost single-peaked.