Learning read-once formulas with queries
Journal of the ACM (JACM)
Learning Arithmetic Read-Once Formulas
SIAM Journal on Computing
An algorithm to learn read-once threshold formulas, and transformations between learning models
Computational Complexity
AkBA: a progressive, anonymous-price combinatorial auction
Proceedings of the 2nd ACM conference on Electronic commerce
Preference elicitation in combinatorial auctions
Proceedings of the 3rd ACM conference on Electronic Commerce
Machine Learning
Machine Learning
Iterative Combinatorial Auctions: Theory and Practice
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Effectiveness of Preference Elicitation in Combinatorial Auctions
AAMAS '02 Revised Papers from the Workshop on Agent Mediated Electronic Commerce on Agent-Mediated Electronic Commerce IV, Designing Mechanisms and Systems
Differential -Revelation VCG Mechanisms for Combinatorial Auctions
AAMAS '02 Revised Papers from the Workshop on Agent Mediated Electronic Commerce on Agent-Mediated Electronic Commerce IV, Designing Mechanisms and Systems
Partial-revelation VCG mechanism for combinatorial auctions
Eighteenth national conference on Artificial intelligence
Using value queries in combinatorial auctions
Proceedings of the 4th ACM conference on Electronic commerce
Applying learning algorithms to preference elicitation
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Preference Elicitation and Query Learning
The Journal of Machine Learning Research
Effectiveness of Query Types and Policies for Preference Elicitation in Combinatorial Auctions
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 1
On the computational power of iterative auctions
Proceedings of the 6th ACM conference on Electronic commerce
Communication complexity of common voting rules
Proceedings of the 6th ACM conference on Electronic commerce
Eliciting single-peaked preferences using comparison queries
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Regret-based incremental partial revelation mechanisms
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Combinatorial auctions with k-wise dependent valuations
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Making markets and democracy work: a story of incentives and computing
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Eliciting single-peaked preferences using comparison queries
Journal of Artificial Intelligence Research
Algorithms and theory of computation handbook
Comparing multiagent systems research in combinatorial auctions and voting
Annals of Mathematics and Artificial Intelligence
On the Computational Power of Demand Queries
SIAM Journal on Computing
AutoMed: an automated mediator for multi-issue bilateral negotiations
Autonomous Agents and Multi-Agent Systems
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Preference elicitation --- the process of asking queries to determine parties' preferences --- is a key part of many problems in electronic commerce. For example, a shopping agent needs to know a user's preferences in order to correctly act on her behalf, and preference elicitation can help an auctioneer in a combinatorial auction determine how to best allocate a given set of items to a given set of bidders. Unfortunately, in the worst case, preference elicitation can require an exponential number of queries even to determine an approximately optimal allocation. In this paper we study natural special cases of preferences for which elicitation can be done in polynomial time via value queries. The cases we consider all have the property that the preferences (or approximations to them) can be described in a polynomial number of bits, but the issue here is whether they can be elicited using the natural (limited) language of value queries. We make a connection to computational learning theory where the similar problem of exact learning with membership queries has a long history. In particular, we consider preferences that can be written as read-once formulas over a set of gates motivated by a shopping application, as well as a class of preferences we call Toolbox DNF, motivated by a type of combinatorial auction. We show that in each case, preference elicitation can be done in polynomial time. We also consider the computational problem of allocating items given the parties' preferences, and show that in certain cases it can be done in polynomial time and in other cases it is NP-complete. Given two bidders with Toolbox-DNF preferences, we show that allocation can be solved via network flow. If parties have read-once formula preferences, then allocation is NP-hard even with just two bidders, but if one of the two parties is additive (e.g., a shopping agent purchasing items individually and then bundling them to give to the user), the allocation problem is solvable in polynomial time.