RI: Small: Automated Planning of Experiments for Design Optimization

RI：小型：自动规划实验以优化设计

• 批准号: 1320943
• 负责人: Alan Fern
• 金额: $ 44万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1320943&HistoricalAwards=false
• 关键词: RI; Small; Automated; Planning; Experiments

Many engineering and scientific projects require planning experimental activities in order to optimize an objective. Such planning involves jointly reasoning about both the budget-limited resource constraints among activities along with the utility of potential information that they may provide. Unfortunately, for many real-world planning problems, with rich structure among potential activities, tools from classic experimental design are not directly applicable due to their simplifying assumptions and poor scalability. This project aims to transform the practice of experimental planning by developing new algorithms that account for the complexities exhibited in a wide range of domains.The project involves three key activities. First, the experimental design description language (EDDL) is being created for formally modeling complex real-world experimental domains. Second, novel planning algorithms are being developed for efficiently computing high-quality solutions to problems expressed in EDDL. Third, work with bioengineers is assessing and improving the usability of the tools and producing benchmark problems based on real and simulated bioengineering data. The creation of the language and benchmarks will help facilitate algorithm comparisons for continued progress by the wider research community.The broader impact of the project is to facilitate experiment planning in a wide range of experimental domains for which there are currently no available computational tools. Currently, in such domains, planning is largely ad-hoc and often done without computer support. Our research has broad economic impact potential by helping engineers and scientists to get the most value out of limited experimental resources. The project also advances high school, undergraduate, and graduate education in the areas of computer science and bio-engineering, with an emphasis on recruiting female students. The students will get the unique experience of working across disciplines and research labs.

许多工程和科学项目需要计划实验活动，以优化目标。这种规划涉及对活动之间预算有限的资源限制以及它们可能提供的潜在信息的效用进行联合推理。不幸的是，对于许多现实世界的规划问题，由于潜在活动之间具有丰富的结构，经典实验设计的工具由于其简化的假设和较差的可扩展性而不能直接适用。该项目旨在通过开发新的算法来改变实验规划的实践，这些算法可以解释在广泛领域中展示的复杂性。该项目涉及三个关键活动。首先，正在创建实验设计描述语言（EDDL），用于正式建模复杂的现实世界实验领域。其次，人们正在开发新的规划算法，以有效地计算EDDL中表达的问题的高质量解决方案。第三，与生物工程师的合作是评估和改进工具的可用性，并根据真实和模拟的生物工程数据产生基准问题。语言和基准的创建将有助于促进算法比较，以促进更广泛的研究界的持续进步。该项目的更广泛的影响是促进实验计划在广泛的实验领域，目前没有可用的计算工具。目前，在这些领域中，规划主要是临时的，通常没有计算机支持。我们的研究通过帮助工程师和科学家从有限的实验资源中获得最大价值，具有广泛的经济影响潜力。该项目还推动了计算机科学和生物工程领域的高中、本科和研究生教育，重点是招收女学生。学生将获得跨学科和跨实验室工作的独特体验。