EAGER: Collaborative Research: An Unified Learnable Roadmap for Sequential Decision Making in Relational Domains

EAGER：协作研究：关系领域顺序决策的统一可学习路线图

• 批准号: 1836575
• 负责人: Ronald Parr
• 金额: $ 10万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1836575&HistoricalAwards=false
• 关键词: EAGER; Collaborative; Research; Unified; Learnable

This project seeks to develop new algorithms and data structures for learning and planning in situations where the environment is represented with a set of relations between objects. Relational representations capture interactions between objects in a succinct and easily interpretable representation. Examples of domains that are well-suited to relational representations includes intelligent drones assisting soldiers, activities in a supply chain management, communication and friendship connections in a social network, and tracking individuals and activities in video. Most recent advances in machine learning and planning, such as so-called "deep neural networks", however, employ simple "flat" representations, where the state of the world is an uninterpreted string of bits. This project will make machine learning and planning methods easier to use and more robust by generalizing them so that they explicitly work with relational models and data. The methods, theory, and data resulting from this proposal will impact the scientific community in several positive ways and will be made publicly available through an appropriate website. The research will be disseminated through refereed journals and conference proceedings and made available to researchers. Code for the proposed algorithms and descriptions of new benchmark problems will also be made publicly available. The investigators will work on organizing workshops and tutorials based on the challenges and findings arising from this project. Many special purpose solutions have been developed to address small parts of these problems, but there are no general purpose tools that harness recent advances in machine learning to tackle this family of problems. This proposal seeks to develop such tools, drawing upon the investigators' prior experience in learning relational regression trees and experience in value function approximation for reinforcement learning. In addition, this project seeks to build a bridge between recent advances in deep learning, which generally has not been compatible with relational representations, and recent advances in relational learning.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目旨在开发新算法和数据结构，以在环境之间以对象之间的一组关系表示的情况下进行学习和计划。关系表示以简洁且易于解释的表示形式捕获对象之间的相互作用。非常适合关系表示的域名的示例包括智能无人机协助士兵，供应链管理中的活动，社交网络中的沟通和友谊联系，以及在视频中跟踪个人和活动。 但是，机器学习和计划方面的最新进展，例如所谓的“深神经网络”，但是，使用简单的“平坦”表示，其中世界状态是未解释的一串。 该项目将通过概括机器学习和计划方法更易于使用，并更加健壮，以便它们明确地与关系模型和数据一起使用。 该提案产生的方法，理论和数据将以几种积极的方式影响科学界，并通过适当的网站公开提供。这项研究将通过裁判期刊和会议程序传播，并提供给研究人员。还将公开提供有关新基准问题的算法和描述的代码。调查人员将根据该项目的挑战和发现组织研讨会和教程。已经开发了许多特殊目的解决方案来解决这些问题的一小部分，但是没有一些通用工具可以利用机器学习的最新进展来解决这个问题。该建议旨在开发此类工具，借鉴研究人员在学习关系回归树方面的先前经验以及在增强学习方面的价值功能近似方面的经验。此外，该项目旨在在深度学习的最新进展之间建立桥梁，这通常与关系表达不兼容，并且在关系学习方面的最新进展。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的审查标准通过评估来进行评估的。