Integer and combinatorial optimization
Integer and combinatorial optimization
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Semiring-based constraint satisfaction and optimization
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Evaluating multiple attribute items using queries
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Nonserial Dynamic Programming
Product Configuration Frameworks-A Survey
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UCP-Networks: A Directed Graphical Representation of Conditional Utilities
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
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Visual exploration and incremental utility elicitation
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On the foundations of expected expected utility
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Incremental utility elicitation with minimax regret decision criterion
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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Utility elicitation as a classification problem
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
The lumière project: Bayesian user modeling for inferring the goals and needs of software users
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Graphical models for preference and utility
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Bucket elimination: a unifying framework for probabilistic inference
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
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UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Minmax regret solutions for minimax optimization problems with uncertainty
Operations Research Letters
Robust solutions of uncertain linear programs
Operations Research Letters
Regret-based reward elicitation for Markov decision processes
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Assessing regret-based preference elicitation with the UTPREF recommendation system
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A geometric approach to find nondominated policies to imprecise reward MDPs
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I
Efficiently eliciting preferences from a group of users
ADT'11 Proceedings of the Second international conference on Algorithmic decision theory
Robust approximation and incremental elicitation in voting protocols
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Eliciting additive reward functions for Markov decision processes
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Robust online optimization of reward-uncertain MDPs
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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ACM Transactions on Interactive Intelligent Systems (TiiS) - Special issue on highlights of the decade in interactive intelligent systems
Elicitation and approximately stable matching with partial preferences
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Multi-winner social choice with incomplete preferences
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
The effectiveness of peer-designed agents in agent-based simulations
Multiagent and Grid Systems
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In many situations, a set of hard constraints encodes the feasible configurations of some system or product over which multiple users have distinct preferences. However, making suitable decisions requires that the preferences of a specific user for different configurations be articulated or elicited, something generally acknowledged to be onerous. We address two problems associated with preference elicitation: computing a best feasible solution when the user's utilities are imprecisely specified; and developing useful elicitation procedures that reduce utility uncertainty, with minimal user interaction, to a point where (approximately) optimal decisions can be made. Our main contributions are threefold. First, we propose the use of minimax regret as a suitable decision criterion for decision making in the presence of such utility function uncertainty. Second, we devise several different procedures, all relying on mixed integer linear programs, that can be used to compute minimax regret and regret-optimizing solutions effectively. In particular, our methods exploit generalized additive structure in a user's utility function to ensure tractable computation. Third, we propose various elicitation methods that can be used to refine utility uncertainty in such a way as to quickly (i.e., with as few questions as possible) reduce minimax regret. Empirical study suggests that several of these methods are quite successful in minimizing the number of user queries, while remaining computationally practical so as to admit real-time user interaction.