AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Towards a universal test suite for combinatorial auction algorithms
Proceedings of the 2nd ACM conference on Electronic commerce
Operations Research
Effective Lower Bounding Techniques for Pseudo-Boolean Optimization
Proceedings of the conference on Design, Automation and Test in Europe - Volume 2
Robust solutions for combinatorial auctions
Proceedings of the 6th ACM conference on Electronic commerce
Combinatorial Auctions
A logical approach to efficient Max-SAT solving
Artificial Intelligence
Finding diverse and similar solutions in constraint programming
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Weighted super solutions for constraint programs
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Fault tolerant Boolean satisfiability
Journal of Artificial Intelligence Research
Modelling Max-CSP as partial Max-SAT
SAT'08 Proceedings of the 11th international conference on Theory and applications of satisfiability testing
Balancing Optimality and Robustness in Resource Allocation Problems
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Dynamic SAT with decision change costs: formalization and solutions
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
SAT Solving for Termination Proofs with Recursive Path Orders and Dependency Pairs
Journal of Automated Reasoning
Reformulation based MaxSAT robustness
Constraints
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The presence of uncertainty in the real world makes robustness to be a desired property of solutions to constraint satisfaction problems. Roughly speaking, a solution is robust if it can be easily repaired when unexpected events happen. This issue has already been addressed in the frameworks of Boolean satisfiability (SAT) and Constraint Programming (CP). Most works on robustness implement search algorithms to look for such solutions instead of taking the declarative approach of reformulation, since reformulation tends to generate prohibitively large formulas, especially in the CP setting. On the other hand, recent works suggest the use of SAT and Max-SAT encodings for solving CP instances. In this paper we present how robust solutions to weighted Max-SAT problems can be effectively obtained via reformulation into pseudo-Boolean formulae, thus providing a much flexible approach to robustness. We illustrate the use of our approach in the robust combinatorial auctions setting and provide some promising experimental results.