RI-Small: Decision-Theoretic Planning for Multi-Agent Systems

RI-Small：多智能体系统的决策理论规划

• 批准号: 0812149
• 负责人: Shlomo Zilberstein
• 金额: $ 40.47万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0812149&HistoricalAwards=false
• 关键词: RI; Small; Decision; Theoretic; Planning

A fundamental challenge in artificial intelligence is to achieve intelligent coordination of a group of decision makers in spite of uncertainty and limited information. Decision theory offers a normative framework for optimizing decisions under uncertainty, but due to computational barriers, developing decision-theoretic reasoning algorithms for multi-agent systems is a serious challenge. This project will advance foundational contributions to the understanding of decision-theoretic planning in stochastic multi-agent domains as well as the development of efficient new algorithms that provide exponential savings in memory requirements and computation time. Moving beyond toy problems is a hard computational challenge that has been broadly recognized by the multiagent systems community. Research under this project will transform the ability of researcher and practitioners to apply decision-theoretic planing to a new range of domains.

人工智能的一个基本挑战是在不确定性和有限信息的情况下实现一组决策者的智能协调。决策理论提供了一个规范的框架，优化决策下的不确定性，但由于计算障碍，开发决策理论推理算法的多智能体系统是一个严峻的挑战。该项目将推进对随机多代理域中决策理论规划的理解以及开发高效的新算法的基础性贡献，这些算法提供了内存需求和计算时间的指数节省。超越玩具问题是一个困难的计算挑战，已被多智能体系统社区广泛认可。该项目的研究将改变研究人员和从业人员将决策理论规划应用于一系列新领域的能力。