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Additive-Disjunctive Heuristics for Optimal Planning

机译:最优规划的可加-分离式启发法

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摘要

The development of informative, admissible heuristics for cost-optimal planning remains a significant challenge in domain-independent planning research. Two techniques are commonly used to try to improve heuristic estimates. The first is disjunction: taking the maximum across several heuristic values. The second is the use of additive techniques, taking the sum of the heuristic values from a set of evaluators in such a way that admissihility is preserved. In this paper, we explore how the two can be combined in a novel manner, using disjunction within additive heuristics. We define a general structure, the Additive-Disjunctive Heuristic Graph (ADHG), that can be used to define an interesting class of heuristics based around these principles. As an example of how an ADHG can be employed, and as an empirical demonstration, we then present a heuristic based on the well-known additive h~m heuristic, showing an improvement in performance when additive-disjunctive techniques are used.
机译:在成本无关的计划中,开发信息允许的启发式方法仍然是与领域无关的计划研究中的重大挑战。通常使用两种技术来尝试改进启发式估计。首先是析取:在多个启发式值中取最大值。第二种是使用加法技术,以保持可接纳性的方式从一组评估器中获取启发式值的总和。在本文中,我们探索了如何使用加性启发式算法中的析取方法以新颖的方式将两者结合。我们定义了一个通用结构,可加-相异启发式图(ADHG),可用于基于这些原理来定义有趣的启发式类。作为如何使用ADHG的示例,以及作为经验证明,我们然后基于众所周知的加法h_m启发式方法提出一种启发式方法,当使用加减法时,表现出性能上的改进。

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