II-EN: A compute cluster and software tools for Monte-Carlo methods in artificial intelligence

该项目支持购买硬件集群和开发软件框架，以支持人工智能中的蒙特卡罗方法，用于通过机器学习对蒙特卡罗模拟结果进行模型编译等任务，应用蒙特卡罗方法解决单代理和多代理顺序决策问题，并将机器学习方法整合到蒙特卡罗搜索中，以改进实时决策。这些软件框架将包括针对每个任务的基线和最新算法的实现，目标是通过通用API和开放源码发布，以便于其他研究人员添加新方法并将外部模拟器连接到框架。这些算法和工具有许多重要的应用，包括：(A)优化森林管理，以共同最大限度地减少灾难性火灾的风险，并最大限度地提高生物和经济效益；(B)设计和验证减少空中交通管制中拥堵的多智能体控制方法；(C)设计和验证微型飞行器的多智能体控制方法；以及(D)模拟物种的时空分布，以支持濒危和受威胁物种的管理。硬件集群和软件框架将整合到俄勒冈州立大学的本科生和研究生课程中，以及俄勒冈州立大学以外的各种推广活动中。

The project supports acquisition of a hardware cluster and development of software frameworks to support Monte Carlo methods in articial intelligence, for tasks such as model compilation through machine learning over the results of Monte Carlo simulation, application of Monte Carlo methods to solve single-agent and multi-agent sequential decision making problems, and integration of machine learning methods into Monte Carlo search to improve real-time decision making. These software frameworks will include implementations of baseline and state-of-the-art algorithms for each task, with a goal of release with generic APIs and open-source availability to make it easy for other researchers to add new methods and to connect external simulators to the frameworks. The algorithms and tools have many important applications, including (a) optimization of forest management to jointly minimize the risk of catastrophic fires and maximize biological and economic benefits, (b)design and validation of multiagent control methods for reducing congestion in air traffic control, (c) design and validation of multiagent control methods for micro air vehicles, and (d)modeling spatio-temporal distribution of species to support management of endangered and threatened species. The hardware cluster and software frameworks will be integrated into the undergraduate and graduate curriculum at Oregon State University, as well as various outreach beyond Oregon State.