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Computer Go -- A Proxy for Key Open Challenges and Opportunities in Computational Intelligence

Computer Go -- A Proxy for Key Open Challenges and Opportunities in Computational Intelligence
Computer Go——计算智能领域关键开放挑战和机遇的代表
批准号:
0725382
负责人:
Donald Wunsch
金额:
$30.0万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2007
资助国家:
美国
项目状态:
已结题
起止时间:
2007-08-01 至 2011-07-31
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项目摘要

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中文摘要
翻译
提案编号:0725382提案标题:计算机围棋--计算智能关键开放挑战和机遇的代表PI名称:Wunsch,Donald C.PI机构:密苏里大学罗拉分校这项研究的目标是通过打造9x9围棋棋手,并为19x19棋手奠定基础,来阐明和缩小计算机和人类能力之间的差异。方法是:将同时递归网络和细胞神经网络相结合,并通过强化学习对它们进行训练,分析影响函数;为已知模式创建神经模糊规则;比较移动过滤的方法;开发改进的战术分析;向后引导结束局技术;开发优化的硬件;执行外部任务。所提出的研究的智力优势是:围棋比国际象棋难得多,其解决方案提供了更多的科学依据。它的微妙之处反映了学习中的核心问题。如上文方法部分所述,提出了创造性和原创性的概念。PI和Co-PI自1998年以来一直是合作伙伴,在许多协同项目中都有重要的研究记录。所提出的研究的更广泛的影响是:改进了启发式算法,以削减组合复杂性。在学习架构和改进培训之间建立更紧密的联系。对相关应用的贡献:经济学、安全应用、传感器网络、嵌入式系统、生物启发应用、K-12、国际和代表不足的群体拓展。由于有围棋规则和数据,结果的传播将会更好。该项目将通过提高自动化战略分析的能力、通过更好的工具和通过改善劳动力来造福社会。
英文摘要
Proposal Number: 0725382Proposal Title: Computer Go -- A Proxy for Key Open Challenges and Opportunities in Computational IntelligencePI Name: Wunsch, Donald C.PI Institution: University of Missouri-RollaThe objective of this research is to illuminate and narrow the differences between computer and human capabilities by making a 9x9 Go player, and creating the groundwork for a 19 x 19 player. The approach is to: Combine Simultaneous Recurrent Networks with Cellular Neural Networks, and train them via Reinforcement Learning, to analyze influence functions; Create Neurofuzzy rules for known patterns; Compare approaches to move filtering; Develop improved tactical analysis; Bootstrap endgame techniques backwards; Develop optimized hardware; Perform outreach.The intellectual merit of the proposed research is: Go is much harder than Chess and its solution offers more to science. Its subtleties mirror core issues in learning. Creative and original concepts are proposed, as outlined in the approach section above. The PI and Co-PI have been collaborators since 1998, and both have significant research track records in many synergistic projects.The broader impacts of the proposed research are: Improved heuristics for cutting through combinatorial complexity. Creating stronger links between learning architectures and improving their training. Contributions to related applications:Economics, Security Applications, Sensor networks,Embedded systems,Biologically-inspired applications, K-12, international, and underrepresented groups outreach. The dissemination of results will be superior, due to the availability of Go rules and data. The project will benefit society, through an improved ability to automate strategic analysis, through better tools, and through an improved workforce.
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Intergovernmental Personnel Act Award - Donald C. Wunsch
Collaborative Research: Wind Power - Neural Network Control, Multidisciplinary Integration, and Advanced Simulation
Scalable, Cellular Architectures, for Improving Solutions With Experience, in Combinatorially Complex Optimization Problems
ADAPTIVE CRITIC DESIGN NEUROCONTROLLERS FOR NONLINEAR LARGE SCALE SYSTEMS AND ADVANCED NETWORK MANAGEMENT
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