Belief shaping in noncooperative communication and control systems through strategic signaling

In this dissertation, we analyze the interaction between intelligent and selfish agents in non-cooperative environments with a specific focus on the transmission of some private information among them. We seek to quantify the ability of informed agents to shape the uninformed (rational) agents' beliefs about the private information through signals crafted strategically even when the uninformed agents construct their beliefs with awareness of how the messages were crafted. Through the quantification of this ability, our goal is to introduce strategic information transmission to applications in cyber and cyber-physical systems as a deception-as-defense mode of operation. It is worth noting that transparency in the signals sent provides robustness against advanced adversaries that can learn/discover the signaling strategy. Due to the versatility of the Gaussian distribution, we first formulate derivation of the optimal signaling strategies for Gauss Markov information in dynamic communication settings. We formulate an equivalent semi-definite program instead of addressing this problem over the original infinite-dimensional strategy spaces. We show that the optimal signaling strategies are linear within the general class of measurable policies when the agents have different quadratic cost measures. This formulation brings in the possibility of adopting strategic information transmission in dynamic control systems based on the common theme of communication and control settings. In this context, we introduce a robust sensor design framework and compute the associated sensor outputs to provide resiliency in linear-quadratic-Gaussian control systems against advanced attackers with malicious and unknown control objectives. In order to extend these results to distributions other than Gaussian, we have address the problem of optimal hierarchical signaling for a general class of square integrable multivariate distributions. Again instead of addressing the problem directly over the original strategy spaces, we have formulated an equivalent linear optimization problem over the cone of completely positive matrices when the underlying state space is finite. The ability to compute the optimal signaling strategies for large finite state spaces enables us to address the signaling problem approximately also for continuous distributions. We also provide analytical guarantees on the level of accuracy for the approximation. Finally, we discuss some of the future research directions on belief shaping through strategic signaling.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2020

Author dc:creator

• Sayin, Muhammed O.

Contributors dc:contributor

• Başar, Tamer
• Hajek, Bruce
• Langbort, Cedric
• Veeravalli, Venugopal V.

• Information Design
• Belief Shaping
• Game Theory
• Signaling
• Strategic Information Transmission
• Deception

Statement dc:rights

• Copyright 2019 Muhammed Sayin

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/106143

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/106143