RI: Small: Foundations and Applications of Generalized Planning

RI：小型：总体规划的基础和应用

• 批准号: 0915071
• 负责人: Shlomo Zilberstein
• 金额: $ 45.5万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0915071&HistoricalAwards=false
• 关键词: RI; Small; Foundations; Applications; Generalized

This project is developing automated methods of artificial intelligence (AI) for creating generalized plans that include loops and branches, can handle unknown quantities of objects, and work for large classes of problem instances. One of the key challenges is to reason about plans with loops and to do so without using automated theorem proving, which tends to be intractable. In particular, research is accomplishing the following goals: (1) develop new theoretical foundations for generalized planning; (2) develop effective abstraction mechanisms and new plan representations to support these new capabilities; (3) develop effective algorithms for plan synthesis as well as generalization of sample plans; (4) develop analysis tools to reason about the applicability, correctness and efficiency of generalized plans; (5) extend the framework to include sensing actions, conditional plans, and domain-specific knowledge in the form of partially specified plans; (6) create a new set of challenging benchmark problems and perform a rigorous evaluation of the approach; and (7) increase the interaction between the AI community and other communities, particularly model checking, that study the abstraction mechanisms and theoretical foundations necessary for generalized planning. This new framework may significantly improve the scope and applicability of automated planning systems.

该项目正在开发人工智能（AI）的自动化方法，用于创建包括循环和分支在内的通用计划，可以处理未知数量的对象，并为大型问题实例工作。关键的挑战之一是对循环计划进行推理，并且不使用自动定理证明，这往往是难以处理的。研究的主要目的是：(1)为广义规划提供新的理论基础；(2)开发有效的抽象机制和新的计划表示来支持这些新功能；(3)开发有效的方案综合和样本方案概化算法；(4)开发分析工具，对通用方案的适用性、正确性和效率进行推理；(5)扩展框架，以部分指定计划的形式包括感知行动、条件计划和领域特定知识；(6)创建一组新的具有挑战性的基准问题，并对该方法进行严格的评估；(7)增加人工智能社区与其他社区之间的互动，特别是模型检查，研究广义规划所需的抽象机制和理论基础。这个新的框架可以显著地提高自动化规划系统的范围和适用性。