Learning an Agent's Utility Function by Observing Behavior
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
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
The permutable POMDP: fast solutions to POMDPs for preference elicitation
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 1
Regret-based optimal recommendation sets in conversational recommender systems
Proceedings of the third ACM conference on Recommender systems
Eliciting single-peaked preferences using comparison queries
Journal of Artificial Intelligence Research
Utilities as random variables: density estimation and structure discovery
UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
Evaluating simulation software components with player rating systems
Proceedings of the 6th International ICST Conference on Simulation Tools and Techniques
ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
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Preference elicitation (PE) is an very important component of interactive decision support systems that aim to make optimal recommendations to users by actively querying their preferences In this paper, we present three principles important for PE in real-world problems: (1) multiattribute, (2) low cognitive load, and (3) robust to noise In light of three requirements, we introduce an approximate PE framework based on a variant of TrueSkill for performing efficient closed-form Bayesian updates and query selection for a multiattribute utility belief state — a novel PE approach that naturally facilitates the efficient evaluation of value of information (VOI) for use in query selection strategies Our VOI query strategy satisfies all three principles and performs on par with the most accurate algorithms on experiments with a synthetic data set.