ITR: Representation and Learning in Computational Game Theory

ITR：计算博弈论中的表示和学习

• 批准号: 0325363
• 负责人: Manfred Warmuth
• 金额: $ 39万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-15 至 2009-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0325363&HistoricalAwards=false
• 关键词: ITR; Representation; Learning; Computational; Game

Computational Game Theory is a rapidly emerging discipline at the intersection of computer science, economics, and related fields. It is becoming a fundamental tool for understanding and designing complex multiagent environments such as the Internet, systems of autonomous agents, and electronic economies. The objective of this program is the development of powerful new representations for complex game-theoretic and economic reasoning problems, and strategic learning algorithms for adjusting their parameters.Special emphasis is being given to models permitting the specification of natural network structure in the interactions within a large population of players, and models generalizing the spirit of financial markets, in which interactions take place via global intermediate quantities. Powerful recent machine learning methods such as boosting and exponential updates are also being applied to the more subtle and complex setting of learning in games.The expected results of the program are a rich set of new modeling methods for game-theoretic applications, and computationally efficient algorithms for reasoning with them, including the computation of Nash, correlated, and other equilibria, as well as efficient learning methods with known convergence properties. Special emphasis will be given to formal analysis, and the resulting methods will provide a new toolbox for researchers in economics, social science, evolutionary biology, and other fields in which game-theoretic approaches are common. The findings of the program will be widely disseminated through international conferences and journals, as well as more specialized workshops deliberately bringing together researchers from the different relevant disciplines.

计算游戏理论是在计算机科学，经济学和相关领域的交集上快速新兴的学科。它已成为理解和设计复杂的多种环境（例如互联网，自主代理系统和电子经济体）的基本工具。该计划的目的是开发有力的新表示形式，以解决复杂的游戏理论和经济推理问题，以及用于调整其参数的战略学习算法。特别强调允许模型，允许在允许在互动中规范自然网络结构的模型。大量的参与者，并模型概括了金融市场的精神，其中通过全球中级数量进行互动。强大的最新机器学习方法（例如提升和指数更新）也被应用于游戏中学习的更微妙和复杂的设置。该程序的预期结果是针对游戏理论应用程序的一系列新的建模方法，以及计算上有效的效率与它们推理的算法，包括NASH，相关和其他均衡的计算，以及具有已知收敛属性的有效学习方法。正式分析将特别强调，由此产生的方法将为经济学，社会科学，进化生物学和其他游戏理论方法的研究人员提供一个新的工具箱。该计划的发现将通过国际会议和期刊进行广泛传播，以及更专业的研讨会故意将来自不同相关学科的研究人员聚集在一起。