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

II-EN：人工智能中蒙特卡罗方法的计算集群和软件工具

• 批准号: 0958482
• 负责人: Thomas Dietterich
• 金额: $ 60万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2014-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0958482&HistoricalAwards=false
• 关键词: II; EN; compute; cluster; software

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.

该项目支持硬件集群的获取和软件框架的开发，以支持人工智能中的蒙特卡罗方法，用于通过机器学习蒙特卡罗模拟结果进行模型编译，应用蒙特卡罗方法解决单代理和多代理顺序决策问题，将机器学习方法集成到蒙特卡洛搜索中，以改善实时决策。这些软件框架将包括每个任务的基线和最先进算法的实现，目标是使用通用API和开源可用性发布，以便其他研究人员轻松添加新方法并将外部模拟器连接到框架。 这些算法和工具具有许多重要的应用，包括（a）优化森林管理以共同最小化灾难性火灾的风险并最大化生物和经济效益，（B）设计和验证多智能体控制方法以减少空中交通管制中的拥塞，（c）设计和验证用于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被集成到俄勒冈州州立大学的本科生和研究生课程中，以及俄勒冈州以外的各种推广活动中。