Efficiently eliciting preferences from a group of users

Authors:
Greg Hines;Kate Larson
Affiliations:
Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada;Cheriton School of Computer Science, University of Waterloo, Waterloo, Canada
Venue:
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
Year:
2011

Citing 4
Cited 0

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
Incremental utility elicitation with minimax regret decision criterion

IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Constraint-based optimization and utility elicitation using the minimax decision criterion

Artificial Intelligence
Trading agents for the smart electricity grid

Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1

Quantified Score

Hi-index	0.00

Visualization

Abstract

Learning about users' preferences allows agents to make intelligent decisions on behalf of users. When we are eliciting preferences from a group of users, we can use the preferences of the users we have already processed to increase the efficiency of the elicitation process for the remaining users. However, current methods either require strong prior knowledge about the users' preferences or can be overly cautious and inefficient. Our method, based on standard techniques from non-parametric statistics, allows the controller to choose a balance between prior knowledge and efficiency. This balance is investigated through experimental results.