Collaborative Research: Distributed Interpretation in a Communication-Limited Environment

协作研究：通信有限环境中的分布式解释

• 批准号: 0414711
• 负责人: Victor Lesser
• 金额: $ 37.5万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-05-15 至 2009-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0414711&HistoricalAwards=false
• 关键词: Collaborative; Research; Distributed; Interpretation; Communication

This project will extend previous work on a decision theoretic approach for designing satisficing multiagent systems so that it is practical to apply the approach to large-scale, real-world sensor networks. The approach is based on distributed Bayesian networks and decentralized Markov decision processes. In terms of intellectual merit, this research addresses a critical need for formal techniques to support the design of multiagent systems. Approximate, satisficing approaches can reason about the appropriate tradeoffs between information quality and resource usage and, thus, reduce barriers to building large-scale sensor networks, as well as other large-scale multiagent systems. In terms of broader impact, the project could ultimately have a substantial positive societal impact by encouraging the development of sophisticated, large-scale sensor networks. Such large-scale sensor networks have numerous military and civilian applications. They can be used to enhance security and safety, as well as to make computerized systems more autonomous and flexible.

这个项目将扩展以前的工作，决策理论的方法设计满意的多智能体系统，使其实际应用于大规模的，现实世界的传感器网络的方法。 该方法是基于分布式贝叶斯网络和分散马尔可夫决策过程。 在智力方面的优点，这项研究解决了一个关键的需要正式的技术，以支持多智能体系统的设计。近似的、令人满意的方法可以推理信息质量和资源使用之间的适当权衡，从而减少构建大规模传感器网络以及其他大规模多智能体系统的障碍。 就更广泛的影响而言，该项目最终可能通过鼓励发展先进的大规模传感器网络而产生重大的积极社会影响。 这种大规模的传感器网络有许多军事和民用应用。 它们可用于增强安全性，并使计算机化系统更加自主和灵活。