Integer and combinatorial optimization
Integer and combinatorial optimization
Online computation and competitive analysis
Online computation and competitive analysis
Computationally Manageable Combinational Auctions
Management Science
Preference elicitation in combinatorial auctions
Proceedings of the 3rd ACM conference on Electronic Commerce
Algorithm for optimal winner determination in combinatorial auctions
Artificial Intelligence
Winner determination in combinatorial auction generalizations
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Taming the Computational Complexity of Combinatorial Auctions: Optimal and Approximate Approaches
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
UCP-Networks: A Directed Graphical Representation of Conditional Utilities
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
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
Partial-revelation VCG mechanism for combinatorial auctions
Eighteenth national conference on Artificial intelligence
Preference elicitation in proxied multiattribute auctions
Proceedings of the 4th ACM conference on Electronic commerce
Incremental utility elicitation with minimax regret decision criterion
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
CABOB: a fast optimal algorithm for combinatorial auctions
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Problem-focused incremental elicitation of multi-attribute tility models
UAI'97 Proceedings of the Thirteenth conference on Uncertainty in artificial intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Regret minimizing equilibria and mechanisms for games with strict type uncertainty
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Bid expressiveness and clearing algorithms in multiattribute double auctions
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Constraint-based optimization and utility elicitation using the minimax decision criterion
Artificial Intelligence
Generating k-best solutions to auction winner determination problems
ACM SIGecom Exchanges
A Trust-Based Incentive Mechanism for E-Marketplaces
Trust in Agent Societies
Efficiently Generating k-Best Solutions to Procurement Auctions
AAIM '09 Proceedings of the 5th International Conference on Algorithmic Aspects in Information and Management
Regret-based incremental partial revelation mechanisms
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Regret-based utility elicitation in constraint-based decision problems
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Constraint-based optimization and utility elicitation using the minimax decision criterion
Artificial Intelligence
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
Proceedings of the 11th ACM conference on Electronic commerce
Robust approximation and incremental elicitation in voting protocols
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
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Recent algorithms provide powerful solutions to the problem of determining cost-minimizing (or revenue-maximizing) allocations of items in combinatorial auctions. However, in many settings, criteria other than cost (e.g., the number of winners, the delivery date of items, etc.) are also relevant in judging the quality of an allocation. Furthermore, the bid taker is usually uncertain about her preferences regarding tradeoffs between cost and nonprice features. We describe new methods that allow the bid taker to determine (approximately) optimal allocations despite this. These methods rely on the notion of minimax regret to guide the elicitation of preferences from the bid taker and to measure the quality of an allocation in the presence of utility function uncertainty. Computational experiments demonstrate the practicality of minimax computation and the efficacy of our elicitation techniques.