Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
On the conversion between non-binary constraint satisfaction problems
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Maintaining knowledge about temporal intervals
Communications of the ACM
Dynamic Flexible Constraint Satisfaction
Applied Intelligence
A Path-Consistent Singleton Modeling (CSM) Algorithm for Arc-Constrained Networks
Applied Intelligence
Constraint Processing
AI Communications - Constraint Programming for Planning and Scheduling
Temporal preference optimization as weighted constraint satisfaction
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Constraint-based preferential optimization
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 1
Exact phase transitions in random constraint satisfaction problems
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
An optimal coarse-grained arc consistency algorithm
Artificial Intelligence
Soft arc consistency revisited
Artificial Intelligence
Solving satisfiability problems with preferences
Constraints
Dynamic constraint satisfaction problems
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
An empirical study of greedy local search for satisfiability testing
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Scatter search technique for exam timetabling
Applied Intelligence
Heuristic techniques for variable and value ordering in CSPs
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Conditional and composite temporal CSPs
Applied Intelligence
Comparing evolutionary algorithms on binary constraint satisfaction problems
IEEE Transactions on Evolutionary Computation
A graph coloring constructive hyper-heuristic for examination timetabling problems
Applied Intelligence
A new crossover for solving constraint satisfaction problems
EvoCOP'13 Proceedings of the 13th European conference on Evolutionary Computation in Combinatorial Optimization
Hi-index | 0.00 |
We present a new framework, managing Constraint Satisfaction Problems (CSPs) with preferences in a dynamic environment. Unlike the existing CSP models managing one form of preferences, ours supports four types, namely: unary and binary constraint preferences, composite preferences and conditional preferences. This offers more expressive power in representing a wide variety of dynamic constraint applications under preferences and where the possible changes are known and available a priori. Conditional preferences allow some preference functions to be added dynamically to the problem, during the resolution process, if a given condition on some variables is true. A composite preference is a higher level of preference among the choices of a composite variable. Composite variables are variables whose possible values are CSP variables. In other words, this allows us to represent disjunctive CSP variables. The preferences are viewed as a set of soft constraints using the fuzzy CSP framework. Solving constraint problems with preferences consists in finding a solution satisfying all the constraints while optimizing the global preference value. This is handled by four variants of the branch and bound algorithm, we propose in this paper, and where constraint propagation is used to improve the time efficiency in practice. In order to evaluate and compare the performance of these four strategies, we conducted an experimental study on randomly generated dynamic CSPs with quantitative preferences. The results are reported and discussed in the paper.