Socially Networked Autonomy: How Should Machines Interact With Society?

社交网络自治：机器应该如何与社会互动？

• 批准号: 2013779
• 负责人: Philip Brown
• 金额: $ 25.05万
• 依托单位: University of Colorado at Colorado Springs
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2013779&HistoricalAwards=false
• 关键词: Socially; Networked; Autonomy; How; Should

This project studies decision design methodologies for autonomous agents that are networked with and interacting among human beings in societal systems. The running application example is that of an urban highway network in which a portion of the vehicles are autonomously operated. The core question is this: how should a system planner design the routing policies of the autonomous vehicles to have the greatest positive impact on overall network traffic congestion, even if human drivers react in a self-interested way to the behavior of the autonomous vehicles? Intuitively, it would appear beneficial to design the autonomous vehicles to be altruistic; that is, to select highway routes that cause the least congestion to other drivers. However, recent work shows that this simple and intuitive routing design policy may backfire, leading to a cascade of behaviors which inadvertently worsens congestion. This project will investigate how a designer can circumvent these pathologies through the design of smart routing policies. One of the key questions this project will address is how selfish should autonomous vehicles be? This project includes public outreach to help members of the public discern between good and bad modes of influence in socially networked autonomous systems. In addition, it will provide a theoretical framework for engineering practitioners to certify that the interactive aspects of smart systems have been designed to provide broad societal benefits in a principled way.This project addresses fundamental behavior design questions for socially-networked multiagent systems; an intrinsic feature of this problem is that overall system performance is governed not only by the designable behavior of the autonomous agents, but also on the self-interested behavior of human participants. The element of interactive optimization places this project squarely in the domain of game theory, and the investigators adapt popular congestion game models to study these questions in the context of smart autonomous vehicle routing in highway networks. A fleet of autonomous vehicles is modeled as a discrete player in a continuous-player-set network congestion game; the project studies how routing policies for the autonomous vehicles impact the game-theoretic equilibria of the overall system. This allows the investigators to model, in a principled way, the interactions between designed agents (i.e., autonomous vehicles) and selfish agents (i.e., human drivers). Ultimately, the investigators seek a comprehensive understanding of how and when autonomous vehicles should be endowed with various routing policies. This project advances knowledge in two distinct dimensions: first, it provides an in-depth case study examining the opportunities and limitations of the use of connected autonomous vehicles for influencing traffic routing in transportation networks. Second, and more broadly, it will provide the foundations for an underlying theory of social influence in smart and connected cyber-physical systems. As smart cyber-physical devices are increasingly integrated into public life, it is critical that these smart devices are designed to interact with and influence human behavior in ways that are effective, efficient, and principled – and this project represents a central pillar of this endeavor.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.

本计画研究社会系统中与人类网络化互动的自主智能体的决策设计方法。行驶应用示例是其中一部分车辆自主运行的城市公路网络。核心问题是：系统规划者应该如何设计自动驾驶车辆的路线策略，以便对整体网络交通拥堵产生最大的积极影响，即使人类驾驶员以自私的方式对自动驾驶车辆的行为做出反应？直觉上，将自动驾驶汽车设计成利他主义似乎是有益的;也就是说，选择对其他司机造成最少拥堵的高速公路路线。然而，最近的研究表明，这种简单直观的路由设计策略可能会适得其反，导致一系列无意中导致拥塞的行为。这个项目将研究如何设计者可以绕过这些病态通过智能路由策略的设计。这个项目将解决的一个关键问题是，自动驾驶汽车应该有多自私？该项目包括公共宣传，以帮助公众成员辨别社会网络自治系统中的好的和坏的影响模式。此外，它将为工程实践者提供一个理论框架，以证明智能系统的交互方面已被设计为以原则性的方式提供广泛的社会效益。该问题的内在特征是整个系统性能不仅由自治代理的可设计行为控制，也取决于人类参与者的自利行为。交互式优化的元素将这个项目直接置于博弈论领域，研究人员采用流行的拥堵博弈模型来研究高速公路网络中智能自动驾驶车辆路由的背景下的这些问题。自动驾驶车辆的车队被建模为一个连续的球员集网络拥塞游戏中的离散球员;该项目研究自动驾驶车辆的路由策略如何影响整个系统的博弈论均衡。这使得研究人员能够以原则性的方式对设计的代理之间的交互进行建模（即，自主车辆）和自私代理（即，人类驾驶员）。最终，研究人员寻求全面了解如何以及何时应赋予自动驾驶车辆各种路线政策。该项目在两个不同的方面推进知识：首先，它提供了一个深入的案例研究，研究使用互联自动驾驶汽车影响交通网络中交通路线的机会和局限性。其次，更广泛地说，它将为智能和互联的网络物理系统中的社会影响力的基本理论提供基础。随着智能网络物理设备越来越多地融入公共生活，这些智能设备的设计必须能够以有效、高效的方式与人类行为互动并影响人类行为，这一点至关重要，有原则的该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的评估来支持。影响审查标准。

项目成果

Robust Networked Multiagent Optimization: Designing Agents to Repair Their Own Utility Functions

• DOI: 10.1007/s13235-022-00469-5
• 发表时间: 2022-09
• 期刊: Dynamic Games and Applications
• 影响因子: 1.5
• 作者: Philip N. Brown;Joshua H. Seaton;Jason R. Marden

The Cost of Denied Observation in Multiagent Submodular Optimization

• DOI: 10.1109/cdc42340.2020.9304054
• 发表时间: 2020-09
• 期刊: 2020 59th IEEE Conference on Decision and Control (CDC)
• 作者: D. Grimsman;Joshua H. Seaton;Jason R. Marden;Philip N. Brown

A Highway Toll Lane Framework that Unites Autonomous Vehicles and High-occupancy Vehicles

结合自动驾驶车辆和高载重车辆的高速公路收费车道框架

• DOI: 10.1109/itsc48978.2021.9564777
• 发表时间: 2021
• 期刊: 2021 IEEE International Intelligent Transportation Systems Conference (ITSC
• 作者: Li, Ruolin;Brown, Philip N.;Horowitz, Roberto
• 通讯作者: Horowitz, Roberto

The Effectiveness of Subsidies and Taxes in Atomic Congestion Games

原子拥塞游戏中补贴和税收的有效性

• DOI: 10.1109/lcsys.2021.3084297
• 发表时间: 2022
• 期刊: IEEE Control Systems Letters
• 影响因子: 3
• 作者: Ferguson, Bryce L.;Brown, Philip N.;Marden, Jason R.
• 通讯作者: Marden, Jason R.

Robust Stochastic Stability in Dynamic and Reactive Environments

动态和反应环境中的鲁棒随机稳定性

• DOI: 10.1109/cdc45484.2021.9683342
• 发表时间: 2021
• 期刊: 60th IEEE Conference on Decision and Control (CDC
• 作者: Collins, Brandon C.;Hines, Lisa;Barboza, Gia;Brown, Philip N.
• 通讯作者: Brown, Philip N.