CIF: Small: Strategic Communication: Concepts, Methods, and Applications
CIF:小:战略沟通:概念、方法和应用
基本信息
- 批准号:1910715
- 负责人:
- 金额:$ 23.5万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Standard Grant
- 财政年份:2019
- 资助国家:美国
- 起止时间:2019-10-01 至 2022-09-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
We are surrounded by networks of smart agents that exchange information with each other through communication channels, as in the case of Internet of Things and autonomous systems. This project considers problems related to communication between these smart and strategic agents referred to as 'strategic communication', when the agents have misaligned or conflicting objectives. Extensions of known strategic communication models to realistic communication settings that involve channel noise and data compression remains an open challenge. The goal of this project is to develop mathematical models of strategic communication for such realistic scenarios. An immediate application of this research pertains to trustworthy and transparent machine learning (ML). ML is increasingly being used in systems that make decisions which affect people, including setting prices for items, scoring credit and job applications, filtering news and social media updates, recommending routes and places, and controlling smart homes and autonomous cars. As ML algorithms make these decisions, it is natural to ask for their transparency, which, on the other hand, makes them vulnerable to manipulation and algorithmic bias. Designing efficient and transparent ML algorithms that are robust to manipulation and bias is a difficult challenge. This project will address this challenge by explicitly taking such possibilities into account in the ML algorithm development through robust strategic communication models. This research will address fundamental questions regarding strategic communication. This emerging research field requires revisiting key results in classical information theory and poses significant challenges, in terms of both analysis and optimization, requiring approaches and tools from multiple disciplines. The outcomes of the research are expected to constitute an essential step in understanding the interplay of game theory and economics with information theory, communications, and compression. Specific goals of this project are categorized into four groups. The first set of goals investigates optimal strategies in non-coded communication settings, with a particular focus on non-Gaussian sources and channels using tools from optimization in function spaces. The second set of goals concerns the transparency issues in ML. Building on the strategic communication models in non-coded scenarios, the project includes two research directions in this area: the quantification of the cost of transparency in various ML algorithms and the design of manipulation-aware ML algorithms. The third set of goals analyzes the role of compression in strategic communication scenarios, including the characterization and computation of fundamental limits, and the development of practical strategic data compression methods. The final set of goals explores networked strategic communication scenarios.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
我们被智能代理网络所包围,这些智能代理通过通信渠道相互交换信息,就像物联网和自治系统一样。这个项目认为,这些智能和战略代理之间的沟通问题被称为“战略沟通”,当代理有错位或冲突的目标。将已知的战略通信模型扩展到涉及信道噪声和数据压缩的现实通信设置仍然是一个开放的挑战。这个项目的目标是为这种现实的情景开发战略沟通的数学模型。这项研究的一个直接应用是可信和透明的机器学习(ML)。机器学习越来越多地被用于做出影响人们的决策的系统中,包括为物品定价,对信用和工作申请进行评分,过滤新闻和社交媒体更新,推荐路线和地点,以及控制智能家居和自动汽车。当ML算法做出这些决定时,自然会要求它们的透明度,另一方面,这使得它们容易受到操纵和算法偏见的影响。设计高效、透明的机器学习算法,使其对操纵和偏见具有鲁棒性,是一项艰巨的挑战。该项目将通过强大的战略通信模型在ML算法开发中明确考虑这些可能性来应对这一挑战。这项研究将解决有关战略传播的基本问题。这一新兴的研究领域需要重新审视经典信息理论中的关键成果,并在分析和优化方面提出了重大挑战,需要来自多个学科的方法和工具。研究的结果预计将构成理解博弈论和经济学与信息论,通信和压缩的相互作用的重要一步。该项目的具体目标分为四组。第一组目标研究非编码通信环境中的最优策略,特别关注使用函数空间优化工具的非高斯源和信道。 第二组目标涉及ML中的透明度问题。该项目以非编码场景中的战略通信模型为基础,包括该领域的两个研究方向:量化各种ML算法中的透明度成本和设计操纵感知ML算法。 第三组目标分析了压缩在战略通信场景中的作用,包括基本限制的表征和计算,以及实用的战略数据压缩方法的开发。该奖项反映了NSF的法定使命,并通过使用基金会的知识价值和更广泛的影响审查标准进行评估,被认为值得支持。
项目成果
期刊论文数量(6)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
Channel-Optimized Strategic Quantizer Design via Dynamic Programming
通过动态规划进行通道优化的战略量化器设计
- DOI:10.1109/ssp53291.2023.10207995
- 发表时间:2023
- 期刊:
- 影响因子:0
- 作者:Anand, Anju;Akyol, Emrah
- 通讯作者:Akyol, Emrah
On Inference of Network Topology and Confirmation Bias in Cyber-Social Networks
- DOI:10.1109/tsipn.2020.3015283
- 发表时间:2019-08
- 期刊:
- 影响因子:3.2
- 作者:Y. Mao;E. Akyol
- 通讯作者:Y. Mao;E. Akyol
Optimal Strategic Quantizer Design via Dynamic Programming
通过动态规划优化策略量化器设计
- DOI:10.1109/dcc52660.2022.00025
- 发表时间:2022
- 期刊:
- 影响因子:0
- 作者:Anand, Anju;Akyol, Emrah
- 通讯作者:Akyol, Emrah
Strategic Remote Estimation
战略遥测
- DOI:10.1109/ieeeconf51394.2020.9443376
- 发表时间:2020
- 期刊:
- 影响因子:0
- 作者:Rule, Griffin;Akyol, Emrah
- 通讯作者:Akyol, Emrah
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Emrah Akyol其他文献
Emrah Akyol的其他文献
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{{ truncateString('Emrah Akyol', 18)}}的其他基金
CAREER: A Holistic Framework for the Analysis of Information Dynamics in Human Networks
职业:人类网络信息动态分析的整体框架
- 批准号:
2048042 - 财政年份:2021
- 资助金额:
$ 23.5万 - 项目类别:
Continuing Grant
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