Information and Computation ( IF 1 ) Pub Date : 2021-07-26 , DOI: 10.1016/j.ic.2021.104787 Michael Sioutis 1 , Diedrich Wolter 1
We introduce and evaluate dynamic branching strategies for solving Qualitative Constraint Networks (s), 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 . Experimental results with a random and a structured dataset of s 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.
中文翻译:
通过计算局部模型在基于定性约束的推理中动态分支
我们介绍和评估用于解决定性约束网络的动态分支策略(s),它们是用于以自然方式表示和推理空间和时间信息的网络,例如,约束可以是“任务A安排在任务C之后或期间”。具体来说,我们提出启发式方法,根据给定关系中涉及的局部模型的数量,将权重与回溯搜索期间发生的分支决策中的关系动态关联。. 随机和结构化数据集的实验结果Interval Algebra 的 s 表明,结构化实例可以实现高达 5 倍的性能提升,同时保持随机实例约 20% 的不可忽略的增益。最后,我们表明通过选择协议算法可以显着改善这些结果,该算法将所涉及的启发式合成为相变中整体性能更好的元启发式。