Handling Temporal Constraints in a Dynamic Environment

Managing symbolic and metric temporal information is fundamental for many real world applications such as scheduling, planning, data base design, computational linguistics and computational models for molecular biology. This motivates us to develop a temporal constraint solving system based on CSPs for handling the two types of temporal information. A main challenge when designing such systems is the ability to deal with temporal constraints in a dynamic and evolutive environment. That is to check, anytime a new constraint is added, whether a solution to the problem (consistent scenario) 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. We talk then about on line temporal CSP-based systems capable of reacting, in an efficient way, to any new external information during the constraint resolution process. In this paper we will present three different techniques we use to tackle dynamic temporal constraint satisfaction problems. These three techniques are respectively based on constraint propagation, randomized local search and genetic algorithms.