Artificial Intelligence - Special issue on knowledge representation
Combining qualitative and quantitative constraints in temporal reasoning
Artificial Intelligence
Maintaining knowledge about temporal intervals
Communications of the ACM
Dynamic CSPs for Interval-Based Temporal Reasoning
IEA/AIE '02 Proceedings of the 15th international conference on Industrial and engineering applications of artificial intelligence and expert systems: developments in applied artificial intelligence
The design and experimental analysis of algorithms for temporal reasoning
Journal of Artificial Intelligence Research
Refining the basic constraint propagation algorithm
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Making AC-3 an optimal algorithm
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
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Computational problems from many different application areas can be seen as constraint satisfaction problems (CSPs). For example, the problems of scheduling a collection of tasks, or interpreting a visual image, or laying out a silicon chip, can all be seen in this way. Solving a CSP consists of finding an assignment of values to variables such that all the constraints are satisfied. If such assignment (called solution to a CSP) is found, we said that the CSP is globally consistent.Our aim in this paper is to maintain the global consistency of a constraint satisfaction problem involving temporal constraints during constraint restriction i.e. anytime a new constraint is added. This problem is of practical relevance since it is often required to check whether a solution to a CSP continues to be a solution when a new constraint is added and if not, whether a new solution satisfying the old and new constraints can be found.The method that we will present here is based on constraint propagation and checks whether the existence of a solution is maintained anytime a new constraint is added. The new constraint is then accepted if the consistency of the problem is maintained and it is rejected otherwise. Experimental tests performed on randomly generated temporal constraint problems demonstrate the efficiency of our method to deal, in a dynamic environment, with large size problems.