Learning Macro-Actions for Arbitrary Planners and Domains
Learning Macro-Actions for Arbitrary Planners and Domains
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发表时间：
2007-09
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通讯作者：
M. A. Hakim Newton;J. Levine;M. Fox;D. Long
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作者：
M. A. Hakim Newton;J. Levine;M. Fox;D. Long
尽管最近在规划方面取得了进展，但许多复杂领域甚至简单领域中的更大问题仍然具有挑战性。除了发展和改进规划技术，利用已获得的知识重新设计一个领域为进一步的研究开辟了一条潜在的途径。此外，当将宏操作作为附加操作添加到域中时，提供了一种很有希望的方式来传达这些知识。宏操作（简称宏）是为应用程序选择的一组操作。大多数关于宏的现有工作都利用了特定于规划器或域的属性。然而，这样的属性不太可能在任意规划器或域中常见。因此，不明确地利用任何关于计划者或领域的结构性知识的宏观学习方法是非常有趣的。本文提出了一种适用于任意选择的规划器和域的离线宏观学习方法。给定一个规划器、一个域和一些示例问题，该学习方法在遗传算法的指导下，从给定问题的一些规划生成宏。它表示像常规操作一样的宏，通过解决剩余的给定问题来单独评估它们，并建议将单个宏永久添加到域中。遗传算法是一种自动学习方法，它可以在没有明确知识的情况下捕获系统的固有特征。因此，我们的方法不努力发现或利用特定于规划器或域的任何结构属性。
Many complex domains and even larger problems in simple domains remain challenging in spite of the recent progress in planning. Besides developing and improving planning technologies, re-engineering a domain by utilising acquired knowledge opens up a potential avenue for further research. Moreover, macro-actions, when added to the domain as additional actions, provide a promising means by which to convey such knowledge. A macro-action, or macro in short, is a group of actions selected for application as a single choice. Most existing work on macros exploits properties explicitly specific to the planners or the domains. However, such properties are not likely to be common with arbitrary planners or domains. Therefore, a macro learning method that does not exploit any structural knowledge about planners or domains explicitly is of immense interest. This paper presents an offline macro learning method that works with arbitrarily chosen planners and domains. Given a planner, a domain, and a number of example problems, the learning method generates macros from plans of some of the given problems under the guidance of a genetic algorithm. It represents macros like regular actions, evaluates them individually by solving the remaining given problems, and suggests individual macros that are to be added to the domain permanently. Genetic algorithms are automatic learning methods that can capture inherent features of a system using no explicit knowledge about it. Our method thus does not strive to discover or utilise any structural properties specific to a planner or a domain.