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.

认知网络上的合作与学习经济学、社会科学和生物科学中的羊群和自组织研究发现，即使每个群体成员表现出有限的行为复杂性，多个智能体之间的协调也会导致规则的行为模式和群体智能。例如，在蚁群中，由于感知能力有限，单个蚂蚁无法从其环境中捕获丰富的空间信息。然而，当蚂蚁在群体内协调它们的活动时，该群体最终会表现出更好的感知能力。使用信号处理和通信技术，该研究研究了这种理性和有组织行为的表现如何以及为何在群体层面上从能力有限的代理之间的本地交互中产生，什么通信拓扑能够实现这种行为，以及什么类型的信号处理能够实现这种形成。 这项研究旨在通过调查认知网络上学习和理性的关系来理解和逆向工程社会经济生物网络中遇到的分布式智能。后者是自适应网络，避免集中式信息处理并执行网络内推理和控制决策。认知网络与依赖集中式和并行信息融合的网络形成鲜明对比，后者不可扩展，难以适应不断变化的拓扑，并且存在脆弱点和信息瓶颈。 该研究考虑了大规模的代理人网络，并研究了全球（理性或非理性）行为模式是如何出现的，包括经济学中的羊群、传染病和泡沫。了解生物环境如何影响动物社会的集体行为，为良好的认知网络提供了现实世界的指南，而认知网络又可用于设计工程系统。认知网络的应用领域包括精准农业、环境监测、救灾管理和智能空间。