Making Rational Decisions Using Adaptive Utility Elicitation
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A POMDP formulation of preference elicitation problems
Eighteenth national conference on Artificial intelligence
Vote elicitation: complexity and strategy-proofness
Eighteenth national conference on Artificial intelligence
Communication complexity of common voting rules
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Aggregating partially ordered preferences: impossibility and possibility results
TARK '05 Proceedings of the 10th conference on Theoretical aspects of rationality and knowledge
Strategic voting when aggregating partially ordered preferences
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
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AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Incompleteness and incomparability in preference aggregation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Eliciting single-peaked preferences using comparison queries
Journal of Artificial Intelligence Research
Utility elicitation as a classification problem
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Toward case-based preference elicitation: similarity measures on preference structures
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Problem-focused incremental elicitation of multi-attribute tility models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
On the computation of fully proportional representation
Journal of Artificial Intelligence Research
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We study the case where agents have preferences over ranges (intervals) of values, and we wish to elicit and aggregate these preferences. For example, consider a set of climatologist agents who are asked for their predictions for the increase in temperature between 2009 and 2100. Each climatologist submits a range, and from these ranges we must construct an aggregate range. What rule should we use for constructing the aggregate range? One issue in such settings is that an agent (climatologist) may misreport her range to make the aggregate range coincide more closely with her own (true) most-preferred range. We extend the theory of single-peaked preferences from points to ranges to obtain a rule (the median-of-ranges rule) that is strategy-proof under a condition on preferences. We then introduce and analyze a natural class of algorithms for approximately eliciting a median range from multiple agents. We also show sufficient conditions under which such an approximate elicitation algorithm still incentivizes agents to answer truthfully. Finally, we consider the possibility that ranges can be refined when the topic is more completely specified (for example, the increase in temperature on the North Pole given the failure of future climate pacts). We give a framework and algorithms for selectively specifying the topic further based on queries to agents.