CIF: Large: Collaborative Research: Cooperation and Learning Over Cognitive Networks

CIF：大型：协作研究：认知网络上的合作与学习

• 批准号: 1531050
• 负责人: Anna Scaglione
• 金额: $ 11.99万
• 依托单位: Arizona State University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-12-01 至 2016-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1531050&HistoricalAwards=false
• 关键词: CIF; Large; Collaborative; Research; Cooperation

Cooperation and Learning over Cognitive NetworksStudies on herding and self-organization in economics and the social and biological sciences have observed that coordination among multiple agents leads to regular patterns of behavior and swarm intelligence, even when each group member shows limited behavioral complexity. In ant colonies, for example, individual ants cannot capture rich spatial information from their environment because of their limited sensing ability. Nevertheless, when the ants coordinate their activities within a colony, the group ends up exhibiting better sensing abilities. Using signal processing and communications techniques, the research studies how and why such manifestations of rational and organized behavior arise at the group level from local interactions among agents with limited abilities, what communication topologies enable such behavior, and what type of signal processing enables such formations. This research seeks to understand and reverse-engineer the distributed intelligence encountered in socio-economic-biological networks, by investigating relations with learning and rationality over cognitive networks. The latter are adaptive networks that avoid centralized information processing and perform in-network inference and control decisions. Cognitive networks contrast with networks that rely on centralized and parallel information fusion, which are not scalable, are hard to adapt to changing topologies, and suffer from points of vulnerability and information bottlenecks. The research considers large scale networks of agents and studies how global (rational or irrational) patterns of behavior emerge, including herds, contagions and bubbles in economics. An understanding of how the biotic environment influences collective behavior in animal societies provides a real world guide to good cognitive networks, which can be used in turn to design engineered systems. Cognitive networks have applications in areas ranging from precision agriculture, to environmental monitoring, disaster relief management, and smart spaces.

关于经济学的放牧和自我组织以及社会和生物科学的认知网络的合作与学习已经观察到，即使每个组成员都表现出有限的行为复杂性，多种代理之间的协调会导致行为和群体智能的定期模式。例如，在蚂蚁殖民地中，由于其感应能力有限，单个蚂蚁无法从其环境中捕获丰富的空间信息。然而，当蚂蚁在殖民地中协调其活动时，该小组最终表现出更好的感知能力。使用信号处理和通信技术，研究研究了在小组级别上如何以及为什么从能力有限的代理之间的局部互动中出现这种理性和有组织行为的表现，哪些通信拓扑可以使这种行为以及哪种类型的信号处理启用这种形式。 这项研究旨在通过研究与认知网络的学习和理性的关系来理解和反向工程。后者是自适应网络，避免集中信息处理并执行网络内推理和控制决策。认知网络与依赖不可扩展的集中信息和并行信息融合的网络形成鲜明对比，难以适应不断变化的拓扑结构，并且遭受了脆弱性和信息瓶颈的要点。 该研究考虑了代理的大规模网络，并研究了行为的全球（理性或非理性）模式如何出现，包括经济学中的牛群，传染和泡沫。对生物环境如何影响动物社会中的集体行为的理解为良好的认知网络提供了现实世界指南，该指南可用于设计工程系统。认知网络在从精准农业到环境监测，救灾管理和智能空间的领域中具有应用。