首页> 外文期刊>International Journal of Artificial Intelligence Tools: Architectures, Languages, Algorithms >INCREMENTAL FILTERING ALGORITHMS FOR PRECEDENCE AND DEPENDENCY CONSTRAINTS
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INCREMENTAL FILTERING ALGORITHMS FOR PRECEDENCE AND DEPENDENCY CONSTRAINTS

机译:优先和依赖约束的增量过滤算法

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Precedence constraints specify that an activity must finish before another activity starts and hence such constraints play a crucial role in planning and scheduling problems. Many real-life problems also include dependency constraints expressing logical relations between the activities - for example, an activity requires presence of another activity in the plan. For such problems a typical objective is a maximization of the number of activities satisfying the precedence and dependency constraints. In the paper we propose new incremental filtering rules integrating propagation through both precedence and dependency constraints. We also propose a new filtering rule using the information about the requested number of activities in the plan. We demonstrate efficiency of the proposed rules on log-based reconciliation problems and min-cutset problems.
机译:优先约束条件指定一个活动必须在另一个活动开始之前完成,因此此类约束在计划和安排问题中起着至关重要的作用。许多现实生活中的问题还包括表达活动之间逻辑关系的依赖关系约束,例如,一个活动需要计划中存在另一个活动。对于此类问题,一个典型的目标是使满足优先级和依赖性约束的活动数量最大化。在本文中,我们提出了新的增量过滤规则,该规则整合了通过优先约束和依赖约束的传播。我们还使用有关计划中请求的活动数量的信息来提出新的过滤规则。我们证明了针对基于日志的对帐问题和最小割集问题的拟议规则的效率。

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