AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Computationally Manageable Combinational Auctions
Management Science
Towards a universal test suite for combinatorial auction algorithms
Proceedings of the 2nd ACM conference on Electronic commerce
Algorithm for optimal winner determination in combinatorial auctions
Artificial Intelligence
Robust solutions for combinatorial auctions
Proceedings of the 6th ACM conference on Electronic commerce
Super solutions for combinatorial auctions
CSCLP'04 Proceedings of the 2004 joint ERCIM/CoLOGNET international conference on Recent Advances in Constraints
Truthful risk-managed combinatorial auctions
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Reasoning about optimal collections of solutions
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
A declarative approach to robust weighted Max-SAT
Proceedings of the 12th international ACM SIGPLAN symposium on Principles and practice of declarative programming
Reformulation based MaxSAT robustness
Constraints
Decomposing combinatorial auctions and set packing problems
Journal of the ACM (JACM)
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Super solutions to constraint programs guarantee that if a limited number of variables lose their values, repair solutions can be found by modifying a bounded number of assignments. However, in many application domains the classical super solutions framework is not expressive enough since it only reasons about the number of breaks in a solution and the number of changes that are necessary to find a repair. For example, in combinatorial auctions we may wish to guarantee that we can always find a repair solution whose revenue exceeds some threshold while limiting the cost associated with forming such a repair. In this paper we present the weighted super solution framework that involves two important extensions. Firstly, the set of variables that may lose their values is determined using a probabilistic approach enabling us to find repair solutions for assignments that are most likely to fail. Secondly, we include a mechanism for reasoning about the cost of repair. The proposed framework has been successfully used to find robust solutions to combinatorial auctions.