Heuristics: intelligent search strategies for computer problem solving
Heuristics: intelligent search strategies for computer problem solving
Depth-first iterative-deepening: an optimal admissible tree search
Artificial Intelligence
Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
Possibilistic constraint satisfaction problems or “how to handle soft constraints?”
UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
Linear-space best-first search
Artificial Intelligence
Uncertainty in Constraint Satisfaction Problems: a Probalistic Approach
ECSQARU '93 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Directed Arc Consistency Preprocessing
Constraint Processing, Selected Papers
Semiring-Based CSPs and Valued CSPs: Basic Properties and Comparison
Over-Constrained Systems
Constraint solving over semirings
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Russian doll search for solving constraint optimization problems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Constraint (Logic) Programming: A Survey on Research and Applications
Selected papers from the Joint ERCIM/Compulog Net Workshop on New Trends in Contraints
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Constraint Violation Minimization Problems arise when dealing with over-constrained CSPs. Unfortunately, experiments and practice show that they quickly become too large and too difficult to be optimally solved. In this context, multiple methods (limited tree search, heuristic or stochastic local search) are available to produce non-optimal, but good quality solutions, and thus to provide the user with anytime upper bounds of the problem optimum. On the other hand, few methods are available to produce anytime lower bounds of this optimum. In this paper, we explore some ways of producing such bounds. All of them are algorithmic variants of a Branch and Bound search. More specifically, we show that a new algorithm, resulting from a combination of the Russian Doll Search and Iterative Deepening algorithms, clearly outperforms five known algorithms and allows high lower bounds to be rapidly produced.