CAREER: A Unified Architecture for Learning of, and Reasoning with, Task Models: Theory and Applications

职业：用于任务模型学习和推理的统一架构：理论与应用

• 批准号: 0642882
• 负责人: Hector Munoz-Avila
• 金额: $ 40万
• 依托单位: Lehigh University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-01-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0642882&HistoricalAwards=false
• 关键词: CAREER; Unified; Architecture; Learning; Reasoning

Proposal 0642882"CAREER: A Unified Architecture for Learning of, and reasoning with, Task Models: Theory and Applications"PI: Hector Munoz-AvilaLehigh UniversityThe focus of this project is the use of Hierarchical Task Networks (HTN) in planning. Humans learn complex skills by incrementally acquiring and combining simpler skills into skills of increasing complexity. Consequentially, many cognitive architectures use hierarchical models to represent relations between skills of different complexity. Over the years, the problem of automatically learning such hierarchical models has taken a backstage. Frequently, it is assumed that these models are given and research has instead concentrated on their use. This project drops this assumption and studies ways to learn the hierarchical models and use them for automated planning. The centerpiece of this project is a unified architecture for (1) learning hierarchical models from STRIPS plans and for (2) a multi-phase combination of lazy and eager learning using a common hierarchical task network (HTN) plan representation. Lazy learning produces hierarchical knowledge that directly represents the input examples. Eager learning produces hierarchical knowledge that generalizes multiple input examples. The unified architecture learns and plans with constructs whose generalization varies between these two extremes. The project also incorporates educational activities based on the multi-phase learning principles formulated in this work. This includes a pilot study, aimed at G6-12 students, to provide hands-on experience on introductory AI. Other broader impacts of this work include possible uses of HTN planning techniques in a wide range of potential applications that are amenable to hierarchical modeling, including project management for public events, software engineering project planning, and developing strategies for automated players in computer games.

提案0642882“职业：一个统一的架构，用于学习和推理任务模型：理论和应用程序” PI：Hector Munoz-Avilalehigh University该项目的重点是在计划中使用层次结构任务网络（HTN）。人类通过逐步获取并将更简单的技能结合到增加复杂性的技能中来学习复杂的技能。因此，许多认知体系结构使用层次模型来表示不同复杂性技能之间的关系。多年来，自动学习此类层次模型的问题已成为后台。通常，假设给出了这些模型，而研究则集中在其使用上。该项目删除了这一假设，并研究了学习层次模型并将其用于自动化计划的方法。该项目的核心是（1）从Strips计划中学习层次模型的统一体系结构以及（2）使用通用层次结构任务网络（HTN）计划表示的懒惰和渴望学习的多相组合。懒惰学习产生直接代表输入示例的层次知识。渴望学习产生层次知识，从而概括了多个输入示例。统一体系结构通过构造学习和计划，其概括在这两个极端之间有所不同。该项目还基于本工作中制定的多相学习原则结合了教育活动。这包括一项针对G6-12学生的试点研究，以提供有关AI入门的动手体验。这项工作的其他更广泛的影响包括在广泛的潜在应用中使用HTN规划技术，这些应用程序可用于分层建模，包括公共活动的项目管理，软件工程项目计划以及为计算机游戏中的自动化玩家制定策略。