Pushing the Boundaries of AI Planning

突破人工智能规划的界限

• 批准号: 0412812
• 负责人: Dana Nau
• 金额: --
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-06-01 至 2009-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0412812&HistoricalAwards=false
• 关键词: Pushing; Boundaries; AI; Planning

This project has the goal of developing planner-generalization techniques that can be used to modify AI planning algorithms to remove some of the restrictive assumptions found in classical approaches to AI planning, such as: perfect knowledge about actions and objects and history of the planning environment, static planning environment, instantaneous actions, discrete time, determinism, black-and-white solution criteria. A starting point for the project will be the PI's preliminary results on a method to "non-determinize" forward-chaining planners. Using both theoretical and empirical techniques, the project will explore ways to systematically generalize planning algorithms to deal with nondeterminism, actions with probabilistic effects, and temporal issues. Results of the project will provide theoretical and experimental underpinnings necessary to enable AI planning to better address the needs of real-world planning applications such as manufacturing planning and ship movement planning. It is intended that implementations of algorithms developed in this project will be made available as open-source software.

该项目的目标是开发计划者泛化技术，该技术可用于修改人工智能规划算法，以消除人工智能规划经典方法中的一些限制性假设，例如：关于行动和对象的完美知识以及规划环境的历史、静态规划环境、瞬时行动、离散时间、决定论、黑白解决方案标准。该项目的起点将是PI对“非确定”前链计划者方法的初步结果。利用理论和经验技术，该项目将探索系统地推广规划算法的方法，以处理不确定性、具有概率效应的行动和时间问题。该项目的结果将提供必要的理论和实验基础，使人工智能规划能够更好地满足现实世界规划应用的需求，如制造规划和船舶运动规划。在这个项目中开发的算法的实现将作为开源软件提供。