Evolving and Generalising Very High Quality Control Knowledge for AI Planning

本项目关注通过利用进化学习技术自动获取控制知识，使人工智能规划实现。这些控制知识可以用来修剪搜索空间中无用的分支，并提出有前途的分支。虽然人工编码的控制规则在计划中是有用的，但它们的规范是一个努力密集的过程，这使得它们很难或不可能概括。它们的使用往往局限于相对简单的领域模型，人类对这些模型的动力学有很好的理解。我们建议从关于规划领域和该领域内的规划过程的静态和动态信息源中自动学习强大的规则。此外，我们将开发一种方法来学习适用于领域类的通用规则，并且可以为特定领域自动自定义。这减少甚至消除了人类的负担，并产生了可扩展的规划技术。

This project is concerned with making AI planning practical by exploiting evolutionary learning techniques to acquire control knowledge automatically. This control knowledge can be used to prune useless branches of the search space and to propose promising branches. Whilst human-coded control rules have been shown to be useful in planning, their specification is an effort-intensive process which makes them difficult or impossible to generalise. Their use has tended to be confined to relatively simple domain models about which the human has a good understanding of the dynamics. We propose to learn powerful rules automatically from both static and dynamic sources of information about the planning domain and the process of planning within that domain. Furthermore, we will develop a method for learning generic rules that apply to classes of domains and that can be automatically customised for a particular domain. This reduces or even removes the burden on the human and results in scalable planning technology.