How not to lie with statistics: the correct way to summarize benchmark results
Communications of the ACM - The MIT Press scientific computation series
Characterizing computer performance with a single number
Communications of the ACM
A Critical Look at Experimental Evaluations of EBL
Machine Learning
Using hundreds of workstations to solve first-order logic problems
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Bidding and allocation in combinatorial auctions
Proceedings of the 2nd ACM conference on Electronic commerce
Optimal solutions for multi-unit combinatorial auctions: branch and bound heuristics
Proceedings of the 2nd ACM conference on Electronic commerce
Towards a universal test suite for combinatorial auction algorithms
Proceedings of the 2nd ACM conference on Electronic commerce
An efficient approximate allocation algorithm for combinatorial auctions
Proceedings of the 3rd ACM conference on Electronic Commerce
Nagging: A Distributed, Adversarial Search-Pruning Technique Applied to First-Order Inference
Journal of Automated Reasoning
Nagging: a scalable fault-tolerant paradigm for distributed search
Artificial Intelligence
A Distributed Learning Algorithm for Bayesian Inference Networks
IEEE Transactions on Knowledge and Data Engineering
Taming the Computational Complexity of Combinatorial Auctions: Optimal and Approximate Approaches
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
An Algorithm for Optimal Winner Determination in Combinatorial Auctions
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Solving Combinatorial Auctions Using Stochastic Local Search
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
An Algorithm for Multi-Unit Combinatorial Auctions
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A Novel Asynchronous Parallelism Scheme for First-Order Logic
CADE-12 Proceedings of the 12th International Conference on Automated Deduction
BOB: improved winner determination in combinatorial auctions and generalizations
Artificial Intelligence
Computationally Manageable Combinatorial Auctions
Computationally Manageable Combinatorial Auctions
Integer Programming for Combinatorial Auction Winner Determination
ICMAS '00 Proceedings of the Fourth International Conference on MultiAgent Systems (ICMAS-2000)
Truth Revelation in Rapid, Approximately Efficient Combinatorial Auctions
Truth Revelation in Rapid, Approximately Efficient Combinatorial Auctions
Problem-Solving Methods in Artificial Intelligence
Problem-Solving Methods in Artificial Intelligence
Combinatorial reverse auction based on revelation of Lagrangian multipliers
Decision Support Systems
Assessing the benefits of group-buying-based combinatorial reverse auctions
Electronic Commerce Research and Applications
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A combinatorial auction (CA) is an auction that permits bidders to bid on bundles of goods rather than just a single item. Unfortunately, winner determination for CAs is known to be NP-hard. In this paper, we propose a distributed algorithm to compute optimal solutions to this problem. The algorithm uses nagging, a technique for parallelizing search in heterogeneous distributed computing environments. Here, we show how nagging can be used to parallelize a branch-and-bound algorithm for this problem, and provide empirical results supporting both the performance advantage of nagging over more traditional partitioning methods as well as the superior scalability of nagging to larger numbers of processors.