SBIR Phase I: Agent-based Cognitive Assistant for Enabling Intelligent Space Operations

这个第一阶段项目的广泛影响/商业潜力是有效地处理极其庞大的数据集。对于个人或团队来说，大量的航空航天数据太过压倒性，无法通过记忆和经验来指挥和控制。该项目将开发一个系统，通过应用于不同类型数据集成的大数据集的机器学习（ML）提供额外支持。它可以在系统开发的所有阶段帮助民用和军事准备、访问和速度，因为它降低了生命周期成本，并改善了生产、测试、验证、操作和支持期间的信息访问。这种能力可以应用于航空航天和非航空航天测试环境、长时间的航天飞行任务，以及需要即时访问大型文档集中的信息并提供最佳可操作智能的其他环境。这个小企业创新研究（SBIR）第一阶段项目展示了基于智能体的认知辅助在现实世界空间操作环境中的应用，集成了机器学习（ML）和自然语言处理（NLP）分析工具。为了提高人类决策的速度并降低成本，可以利用搜索、索引、计算语言学以及广义上的机器学习和自然语言处理方面的最新发展来简化和提高大量高技术文档的可访问性。该项目将展示所有类型的大型数据集，包括多种格式的文本，数字，地理空间，结构化和非结构化，都可以使用先进的分布式开源搜索引擎进行查询。通过分析NLP ML算法产生的元数据，增强搜索引擎结果的信息价值。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this Phase I project is to process extremely large data sets efficiently. The sheer abundance of aerospace data is simply too overwhelming for an individual or team to have command of and control from memory and experience. This project will develop a system that can provide additional support through machine learning (ML) applied to big data sets where different types of data are integrated. It can aid civilian and military readiness, access, and speed during all phases of system development, as it reduces lifecycle cost and improves information access during production, test, verification, operations, and support. This capability can be applied to aerospace and non-aerospace test environments, long spaceflight missions, and other contexts where information in a large document set requires instant accessibility and provides optimal actionable intelligence. This Small Business Innovation Research (SBIR) Phase I project demonstrates the application of agent-based cognitive assistance in a real-world space operations environment, integrating machine learning (ML) and Natural Language Processing (NLP) analytical tools. To improve the speed and lower the cost of human decision making, the latest state-of-the-art developments in search, indexing, computational linguistics, and broadly speaking, machine learning and natural language processing, can be leveraged to simplify and improve the accessibility of a huge corpus of highly technical documentation. This project will demonstrate that all types of large datasets, including text in a multitude of formats, numerical, geospatial, structured and unstructured, can be interrogated using an advanced distributed open-source search engine. The information value of the search engine results is enhanced by analyzing metadata produced from NLP ML algorithms.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.