RI: Small: Foundations and Applications of Generalized Planning

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的通用计划，可以处理未知数量的对象，并可处理大类问题实例。关键的挑战之一是用循环来推理计划，并且在不使用自动定理证明的情况下做到这一点，这往往很难解决。具体地说，研究正在实现以下目标：(1)为广义规划发展新的理论基础；(2)开发有效的抽象机制和新的计划表示来支持这些新的能力；(3)开发有效的计划综合和样本计划概括的算法；(4)开发分析工具来推理广义计划的适用性、正确性和效率；(5)扩展框架以包括感知动作、条件计划和部分指定计划形式的特定领域的知识；(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；以及(7)增加人工智能社区与其他社区之间的互动，特别是模型检测，这些社区研究普遍规划所需的抽象机制和理论基础。这一新框架可能会显著提高自动化规划系统的范围和适用性。

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.