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A weighted CSP approach to cost-optimal planning

机译:成本最优计划的加权CSP方法

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For planning to come ot age, plans must be judged by a measure of quality, such as the total cost of actions. This paper describes an optimal-cost planner which guarantees global optimality whenever the planning problem has a solution. We code the extraction of an optimal plan, from a planning graph with a fixed number k of levels, as a weighted constraint satisfaction problem (WCSP). The specific structure of the resulting WCSP means that a state-of-the-art exhaustive solver was able to find an optimal plan in planning graphs containing several thousand nodes.Thorough experimental investigations demonstrated that using the planning graph in optimal planning is a practical possibility for problems of moderate size, although not competitive, in terms of computation time, with optimal state-space-search planners. Our general conclusion is, therefore, that planning-graph-based optimal planning is not the most efficient method for cost-optimal planning.Nonetheless, the notions of indispensable (sets of) actions and too-costly actions introduced in this paper have various potential applications in optimal planning. These actions can be detected very rapidly by analysis of the relaxed planning graph.
机译:为了使计划成熟,必须通过质量度量来判断计划,例如行动的总成本。本文介绍了一种最优成本计划器,该计划器可以在计划问题解决时保证全局最优性。我们将从具有固定数目k的级别的计划图中提取最优计划编码为加权约束满足问题(WCSP)。生成的WCSP的特定结构意味着最先进的穷举求解器能够在包含数千个节点的计划图中找到最佳计划。全面的实验研究表明,在最佳计划中使用计划图是一种实际的可能性。对于中等大小的问题,尽管在计算时间方面没有竞争力,但需要使用最佳状态空间搜索计划程序。因此,我们的一般结论是,基于计划图的最优计划并不是成本最优计划的最有效方法。尽管如此,本文介绍的必不可少的(一组)行动和过于昂贵的行动的概念具有多种潜力优化计划中的应用程序。通过分析松弛的计划图可以非常迅速地检测到这些动作。

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