CaMeL: Learning Method Preconditions for HTN Planning

O. Ilghami, Dana S. Nau, Hector Muñoz-Avila, D. Aha
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引用次数: 58

Abstract

A great challenge in using any planning system to solve real-world problems is the difficulty of acquiring the domain knowledge that the system will need. We present away to address part of this problem, in the context of Hierarchical Task Network (HTN) planning, by having the planning system incrementally learn conditions for HTN methods under expert supervision. We present a general formal framework for learning HTN methods, and a supervised learning algorithm, named CaMeL, based on this formalism. We present theoretical results about CaMeL's soundness, completeness, and convergence properties. We also report experimental results about its speed of convergence under different conditions. The experimental results suggest that CaMeL has the potential to be useful in real-world applications.
骆驼:HTN规划的学习方法前提
在使用任何计划系统来解决实际问题时,一个巨大的挑战是获取系统所需的领域知识的困难。在分层任务网络(HTN)规划的背景下,通过让规划系统在专家监督下逐步学习HTN方法的条件,我们提出了解决这个问题的部分方法。我们提出了一个学习HTN方法的通用形式化框架,以及一个基于该形式化的监督学习算法CaMeL。给出了CaMeL的完备性、完备性和收敛性的理论结果。本文还报道了在不同条件下其收敛速度的实验结果。实验结果表明,CaMeL有可能在实际应用中发挥作用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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