ITR: Collaborative Research: Representation and Learning in Computational Game theory

ITR：协作研究：计算博弈论中的表示和学习

• 批准号: 0325281
• 负责人: Michael Littman
• 金额: $ 37万
• 依托单位: Rutgers University New Brunswick
• 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=0325281&HistoricalAwards=false
• 关键词: ITR; Collaborative; Research; Representation; Learning

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.

计算博弈论是一门迅速崛起的学科，它是计算机科学、经济学及其相关领域的交叉学科。它正在成为理解和设计复杂的多智能体环境的基本工具，如互联网、自主智能体系统和电子经济。该项目的目标是为复杂的博弈论和经济推理问题开发强大的新表示法，以及调整其参数的战略学习算法。特别强调允许在大量参与者的交互中指定自然网络结构的模型，以及概括金融市场精神的模型，其中交互通过全局中间量发生。最近强大的机器学习方法，如Boost和指数更新，也被应用到游戏中更微妙和复杂的学习环境中。该程序的预期结果是一套丰富的新建模方法，用于博弈论应用，以及用于推理的计算高效算法，包括Nash、相关和其他均衡的计算，以及具有已知收敛性质的高效学习方法。将特别强调形式分析，由此产生的方法将为经济学、社会科学、进化生物学和其他博弈论方法普遍存在的领域的研究人员提供一个新的工具箱。该项目的研究结果将通过国际会议和期刊，以及有意将不同相关学科的研究人员聚集在一起的更专业的研讨会广泛传播。