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
Reasoning about numeric and symbolic time information
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
A Local Search Approach to Modelling and Solving Interval Algebra Problems
Journal of Logic and Computation
The design and experimental analysis of algorithms for temporal reasoning
Journal of Artificial Intelligence Research
Incremental tractable reasoning about qualitative temporal constraints
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
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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A main challenge when designing constraint based systems in general and those involving temporal constraints in particular, is the ability to deal with constraints in a dynamic and evolutive environment. That is to check, anytime a new constraint is added, whether a consistent scenario continues to be consistent when a new constraint is added and if not, whether a new scenario satisfying the old and new constraints can be found. We talk then about on line temporal constraint based systems capable of reacting, in an efficient way, to any new external information during the constraint resolution process. In this paper, we will investigate the applicability of systematic versus approximation methods for solving incremental temporal constraint problems. In order to handle both numeric and symbolic constraints, the systematic method is based on constraint propagation performed at both the qualitative and quantitative levels. The approximation methods are respectively based on stochastic local search and genetic algorithms. Experimental evaluation of the performance in time and the quality of the solution returned (number of violated constraints) of the different techniques has been performed on randomly generated temporal constraint problems. The results favour the exact method for problems with reasonable size while the approximation techniques are the methods of choice for very large problems in the case where we want to trade the quality of the solution for the process time. Indeed, while the approximation methods are faster for large problems, they do not guarantee, in general, the completeness of the solution returned.