Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Consistency restoriation and explanations in dynamic CSPs----application to configuration
Artificial Intelligence
On the Hamming distance of constraint satisfaction problems
Theoretical Computer Science - Complexity and logic
Some Computational Aspects of distance-sat
Journal of Automated Reasoning
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
Journal of Artificial Intelligence Research
Distance constraints in constraint satisfaction
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
ExpertClerk: navigating shoppers' buying process with the combination of asking and proposing
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 2
Algorithms for max hamming exact satisfiability
ISAAC'05 Proceedings of the 16th international conference on Algorithms and Computation
Algorithms for the maximum hamming distance problem
CSCLP'04 Proceedings of the 2004 joint ERCIM/CoLOGNET international conference on Recent Advances in Constraints
Soft constraints of difference and equality
Journal of Artificial Intelligence Research
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The problem of finding a collection of solutions to a combinatorial problem that is optimal in terms of an inter-solution objective function exists in many application settings. For example, maximizing diversity amongst a set of solutions in a product configuration setting is desirable so that a wide range of different options is offered to a customer. Given the computationally challenging nature of these multi-solution queries, existing algorithmic approaches either apply heuristics or combinatorial search, which does not scale to large solution spaces. However, in many domains compiling the original problem into a compact representation can support computationally efficient query answering. In this paper we present a new approach to find optimal collections of solutions when the problem is compiled into a multi-valued decision diagram. We demonstrate empirically that for real-world configuration problems, both exact and approximate versions of our methods are effective and are capable of significantly outperforming state-of-the-art search-based techniques.