New methods to color the vertices of a graph
Communications of the ACM
Consistency restoriation and explanations in dynamic CSPs----application to configuration
Artificial Intelligence
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Constraint Propagation and Value Acquisition: Why we should do it Interactively
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
An Interactive Constraint-Based System for Selective Attention in Visual Search
ISMIS '97 Proceedings of the 10th International Symposium on Foundations of Intelligent Systems
Uncertainty in Constraint Satisfaction Problems: a Probalistic Approach
ECSQARU '93 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Solving weighted CSP by maintaining arc consistency
Artificial Intelligence
Consistency for Partially Defined Constraints
ICTAI '05 Proceedings of the 17th IEEE International Conference on Tools with Artificial Intelligence
Artificial Intelligence - Special issue: Distributed constraint satisfaction
CP '08 Proceedings of the 14th international conference on Principles and Practice of Constraint Programming
Dealing with incomplete preferences in soft constraint problems
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
A cost-based model and algorithms for interleaving solving and elicitation of CSPs
CP'07 Proceedings of the 13th international conference on Principles and practice of constraint programming
Hi-index | 0.00 |
We consider Constraint Satisfaction Problems in which constraints can be initially incomplete, where it is unknown whether certain tuples satisfy the constraint or not. We assume that we can determine the satisfaction of such an unknown tuple, i.e., find out whether this tuple is in the constraint or not, but doing so incurs a known cost, which may vary between tuples. We also assume that we know the probability of an unknown tuple satisfying a constraint. We define algorithms for this problem, based on backtracking search. Specifically, we consider a simple iterative algorithm based on a cost limit on the unknowns that may be determined, and a more complex algorithm that delays determining an unknown in order to estimate better whether doing so is worthwhile. We show experimentally that the more sophisticated algorithms can greatly reduce the average cost.