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

基本信息

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

项目摘要

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.

该项目支持获取硬件群集和软件框架的开发，以支持蒙特卡洛方法，以通过机器学习，诸如蒙特卡洛模拟的结果，蒙特卡洛方法的应用来解决诸如模型编译之类的任务，以求解单个代理和多代理的顺序决策问题，并将机器学习方法纳入蒙特利的搜索。这些软件框架将包括针对每个任务的基线和最先进算法的实现，其目标是使用通用API和开源可用性发布，以使其他研究人员可以轻松添加新方法并将外部模拟器连接到框架上。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降低灾难性火灾的风险，并最大程度地提高生物学和经济益处，（b）设计和验证多种基因控制方法，用于减少空中交通控制中的拥堵，（c）对Micro Air Spristion和Micro Air Spectem spate spatiio spatiio spatem spatiio spatiio spatiio spatiio spatiio spatiio spatiio spatiio spatiio spatiio torker（d）模型（受到威胁和威胁的物种。硬件集群和软件框架将集成到俄勒冈州立大学的本科和研究生课程中，以及俄勒冈州以外的各种外展。