Abstract
We introduce and evaluate dynamic branching strategies for solving Qualitative Constraint Networks (QCNs), which are networks for representing and reasoning about spatial and temporal information in a natural manner, e.g., a constraint can be “Task A is scheduled after or during Task C”. Specifically, we propose heuristics that dynamically associate a weight with a relation in the branching decisions that occur during backtracking search, based on the count of local models that the relation is involved with in a given QCN. Experimental results with a random and a structured dataset of QCNs of Interval Algebra show that it is possible to achieve up to 5 times better performance for structured instances, whilst maintaining non-negligible gains of around 20% for random ones. Finally, we show that these results may be notably improved via a selection protocol algorithm that synthesizes the involved heuristics into an overall better performing meta-heuristic in the phase transition.
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