II-EN: Software Tools for Monte-Carlo Optimization

计算研究基础设施项目支持在基于云的平台上开发用于人工智能中的蒙特卡罗方法的开源软件库。蒙特卡罗方法是用于人工智能、机器学习、数据挖掘和物理科学的随机数值算法。随着数据大小和模型复杂性的不断增长，先进的大规模蒙特卡罗技术（包括并行实现）变得无处不在。然而，能够轻松实现大规模蒙特卡罗高级技术的软件工具尚未建立或广泛使用。本项目开发的软件库将有助于弥合这一差距，并降低广泛研究社区采用先进蒙特卡罗技术的障碍。该库将包括各种现有的最先进的算法，以及新颖的软件组件。这些算法和工具有许多重要的应用，包括：(a)生态管理问题的优化，包括濒危物种保护、森林火灾管理和入侵物种管理；(b)自动化软件测试；(c)科学和工程实验设计的优化；(d)从嘈杂的视觉证据中跟踪多个目标；(e)计算机视觉中的活动和目标识别。这些问题具有重要的社会和经济意义，该研究具有显著扩展当前能力的潜力。算法和工具将使用支持云平台的公共接口来实现，这将允许其他研究人员扩展和应用该库到重要的应用程序中。该软件库将被整合到俄勒冈州立大学的本科和研究生课程中。此外，将开发一门以图书馆组件的理论和应用为中心的在线课程，以方便广大受众使用。开发的库将包括基于研究人员研究的组件，这些组件实现了蒙特卡洛优化（MCO）的许多技术创新，包括：a)利用MCO中的多保真模拟器进行离线和在线规划，(b)开发用于项目发现问题的MCO技术，(c)开发用于顺序决策中的在线政策改进的MCO技术，(d)学习减少分支因素以提高在线MCO的效率，以及(e)将符号推理和MCO集成用于可扩展的顺序决策。这些新功能将推动人工智能的发展，并使以前的MCO方法范围之外的新应用得以解决。

The Computing Research Infrastructure project supports the development of an open-source software library for Monte Carlo methods in artificial intelligence on a cloud-based platform. Monte Carlo methods are randomized numerical algorithms used in AI, machine learning, data mining, and the physical sciences. As data size and model complexity continue to grow, advanced large-scale Monte-Carlo techniques (including parallel implementation) has become ubiquitous. However software tools to easily implement advanced techniques for large-scale Monte Carlo are not established or broadly available.The software library developed in this project will help bridge this gap, and lower the barrier to adoption of advanced Monte Carlo techniques by a broad research community. The library will include a variety of existing state-of-the-art algorithms, as well as novel software components. The algorithms and tools have many important applications, including: (a) optimization of ecological management problems, including endangered species conservation, forest fire management, and invasive species management (b) automated software testing, (c) optimization for experimental design in science and engineering, (d) tracking of multiple objects from noisy visual evidence, and (e) activity and object recognition in computer vision. These problems have significant societal and economic importance and the research has the potential to significantly extend current capabilities.The algorithms and tools will be implemented using a common interface supporting a cloud-based platform, which will allow other researchers to extend and apply the library to important applications. The software library will be integrated into theundergraduate and graduate curriculum at Oregon State University. In addition, an online course centered around the theory and application of the library components will be developed, facilitating use by a wide audience.The developed library will include components based on the investigators' research that realize a number of technicalinnovations for Monte-Carlo Optimization (MCO), including: a) Exploiting multi-fidelity simulators in MCO for offline and online planning, (b) Developing MCO techniques for item discovery problems, (c) Developing MCO techniques for online policy improvement in sequential decision making, (d) Learning to reduce branching factors for more efficient online MCO, and (e) Integrating symbolic reasoning and MCO for scalable sequential decision making. These new capabilities will advance the state-of-the-art in artificial intelligence and enable new applications to be addressed that are beyond the scope of prior MCO methods.