Evolving and Generalising Very High Quality Control Knowledge for AI Planning

发展和推广人工智能规划的高质量控制知识

• 批准号: EP/D062764/1
• 负责人: John Levine
• 金额: $ 50.85万
• 依托单位: University of Strathclyde
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2006
• 资助国家: 英国
• 起止时间: 2006 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FD062764%2F1
• 关键词: Evolving; Generalising; Very; Quality; Control

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.

该项目关注的是通过利用进化学习技术自动获取控制知识来实现人工智能规划。该控制知识可用于修剪搜索空间的无用分支并提出有希望的分支。虽然人类编码的控制规则已被证明是有用的规划，他们的规格是一个努力密集型的过程，使他们难以或不可能概括。它们的使用往往局限于人类对动态有很好理解的相对简单的领域模型。我们建议学习强大的规则自动从静态和动态的信息源规划域和规划的过程中，该域。此外，我们将开发一种方法，用于学习适用于域类的通用规则，并可以为特定域自动定制。这减少甚至消除了人类的负担，并导致可扩展的规划技术。

项目成果

Learning Macro-Actions for Arbitrary Planners and Domains

• 发表时间: 2007-09
• 作者: M. A. Hakim Newton;J. Levine;M. Fox;D. Long

Evolutionary-based learning of generalised policies for AI planning domains

人工智能规划领域通用策略的基于进化的学习

• DOI: 10.1145/1569901.1570062
• 发表时间: 2009
• 作者: Levine J

Lifting the Limitations in a Rule-based Policy Language

解除基于规则的政策语言的限制

• 发表时间: 2009
• 作者: A Lindsay

Online Identification of Useful Macro-Actions for Planning

在线识别对规划有用的宏观行动

• 发表时间: 2007
• 作者: A Coles