Computationally Manageable Combinational Auctions
Management Science
Bidding and allocation in combinatorial auctions
Proceedings of the 2nd ACM conference on Electronic commerce
Algorithm for optimal winner determination in combinatorial auctions
Artificial Intelligence
Taming the Computational Complexity of Combinatorial Auctions: Optimal and Approximate Approaches
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Automated mechanism design: complexity results stemming from the single-agent setting
ICEC '03 Proceedings of the 5th international conference on Electronic commerce
Self-interested automated mechanism design and implications for optimal combinatorial auctions
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Complexity of mechanism design
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Empirical mechanism design: methods, with application to a supply-chain scenario
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
A Technique for Large Automated Mechanism Design Problems
IAT '06 Proceedings of the IEEE/WIC/ACM international conference on Intelligent Agent Technology
Automated design of scoring rules by learning from examples
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
The learnability of voting rules
Artificial Intelligence
Computational aspects of mechanism design
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 4
Mechanisms for making crowds truthful
Journal of Artificial Intelligence Research
Strategy-proof allocation of multiple items between two agents without payments or priors
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Auctions, evolution, and multi-agent learning
ALAMAS'05/ALAMAS'06/ALAMAS'07 Proceedings of the 5th , 6th and 7th European conference on Adaptive and learning agents and multi-agent systems: adaptation and multi-agent learning
Algorithms for strategyproof classification
Artificial Intelligence
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Mechanism design is the art of designing the rules of the game so that a desirable outcome is reached even though the agents in the game behave selfishly. This is a difficult problem because the designer is uncertain about the agentsý preferences and the agents may lie about their preferences. Traditionally, the focus in mechanism design has been on designing mechanisms that are appropriate for a range of settings. While this approach has produced a number of famous mechanisms, much of the space of possible settings is still left uncovered. In contrast, in automated mechanism design (AMD), a mechanism is computed on the fly for the setting at hand 驴 a universally applicable approach. In this paper we present (to our knowledge) the first algorithm designed specifically for AMD. It is designed for the special case where there is only one type-reporting agent, the mechanism must be deterministic, and payments are not possible. The algorithm relies on an association of a particular (easy to compute) mechanism to each subset of outcomes, and a proof that one such mechanism is an optimal one 驴 which allows us to reduce the search space to one of size 2|O|. We propose an admissible heuristic to use in searching over this space, and show how it can be updated efficiently from node to node. We show how to apply branch and bound DFS as well as IDA* to this search space, and show that this approach outperforms CPLEX 8.0, a general-purpose solver, solidly on unstructured instances, both with and without an IR constraint. However, on our third type of instance, a bartering problem, CPLEX almost achieves the performance of our algorithm by exploiting the structure inherent in the domain.